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CONFERENCE ON “SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS)” MARCH 26-27 2011

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TEMPORAL DIFFERENCES IN PROCESSING OF SYMMETRIC OBJECTS IN HUMAN

BRAIN

1Taslima Ahmed,

2 Umesh Kumar,

3Prasant kumar

1,2&3

IIMT College of Engineering, Greater Noida, UP

ABSTRACT

Despite ample of research, perception, recognition and

identification of symmetric objects like faces are still a

matter of active debate. Here we studied the difference in the

neural mechanisms manifested by the most symmetrical

natural objects, “human faces” and unnatural symmetrical

objects like “paired ears and non biological objects” by

using event related potential (ERP) study. To test such an

assumption, human faces and symmetric unnatural objects

were presented to subjects on a projected screen as visual

stimuli. Electrophysiological data showed an important

latency effect for both of these categories of objects. N200

represents an important step in recognition of symmetric

objects in visual world. Moreover the amplitudes of

unnatural symmetrical objects are larger compared to those

for facial activation, which confirms that the neural

activation is more dominant for the unnatural novel objects

than natural objects.

Index Terms— Event related potentials (ERPs); N200;

Electrophysiology; Latency.

1. INTRODUCTION

Advances in electrophysiological techniques and brain

imaging technology (especially functional magnetic

resonance Imaging (fMRI)) have radically improved our

understanding of the functional organization, spatial

locations as well as the process underlying the understanding

of spatial world around us. The burgeoning literature on this

topic of spatial localization indicates that object recognition

varies widely across brain system in visual cortex giving rise

to highly specific altercation as a domain-specific or

domain-general. The organization of the ventral visual

pathways is characterized by strong selectivity for particular

object categories at the level of both individual neurons and

larger cortical regions [1]. There is a region in the brain,

lateral occipital cortex (LOC) which is known as the object

selective region [2]. The LOC has little selectivity for

particular stimulus categories [3,4], but several regions of

cortex near the LOC are selective for particular object

categories; they respond at

least twice as strongly to their preferred stimuli than no other

stimuli. All human cortical regions can be found that

respond selectively to faces (the fusiform face area (FFA)

[5,6] and, in many individuals, the occipital face area (OFA)

[7,8], to places and to a lesser extent – to buildings (the

parahippocampal place area (PPA) [9,10]), to body parts

(the extrastriate body area (EBA)[11] and, in most people,

the fusiform body area [12] and to visually presented words

or letter strings [13].The location and functional properties

are very similar across humans. Hemodynamic techniques

such as fMRI have identified brain regions associated with

face-processing, most notably in fusiform, ventral occipital

and superior temporal cortex. The precise role of the lateral

midfusiform (LMF) with processing the invariant aspects of

faces is contentious however. Furthermore, though it is

generally activated during face perception, when comparing

face versus non-face stimuli [6], it is not always activated

during face recognition, when compromising familiar versus

unfamiliar faces. For reasons not fully understood however,

other studies have found effects of both face recognition and

priming in the LMF.

The temporal characteristics of face-processing have

been elucidated by electrophysiological techniques.

Intracranial ERPs reveal a negative potential peaking -

200ms post stimuli, in both ventral and lateral temporal

regions, which is greater to faces than scrambled faces or

non-faces objects [14]. These data suggest that

hemodynamic correlates of recognition of face priming the

fusiform area of occipital cortex of human brain, which is

mostly domain-specific.

The studies reviewed above have not directly compared

the process of similar object perception, recognition in a

particular localized area (domain specific) of the human

brain. Therefore, in the present study, we used even related

potentials study within a common paradigm. This allowed us

to compare these processes in terms of their cerebral

localization. We operational zed domain-general theory of

neural perception by comparing the ERPs study of faces vs.

unnatural objects like paired ears as described in later

sections.


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2. MATERIALS AND METHODS

2.1. Participants

Ten volunteers participated in the said ERPs study after

giving written and informed consent. Participants were six

men and four women, aged 19-30years (median 24). All

volunteers were right handed, in good health and with no

known neurological disorder.

2.2. Experimental design approach

We used 32-channel EEG system to record neuronal activity

during presentations of two categories of visual stimuli:

faces and symmetric pairs of biological objects (Fig. 1). The

photographs of faces were from an image database that has

been previously used in the lab. The pairs of ears were put

together from pictures of people‟s left and right ears. Just

like the left and right sides of a face, a person‟s left and right

ears are similar but are not exactly the same. The parameters

for the signal acquisition were used to acquire the ERPs of

100 objects from each category, stimuli onset time was

800ms while the inter-stimuli interval was fixed to 1s. By

using advanced soft computing techniques, brain regions

that are activated by faces and symmetric unnatural objects

were localized as regions of interests (ROIs).

Fig 1: Samples of photos of faces and symmetric unnatural

objects used as stimuli

2.3. Event related potentials (ERPs)

Event-related potentials (ERPs) in EEGs directly measure

the electrical response of the cortex to sensory, affective or

cognitive events. They are the voltage fluctuations in the

EEG induced within the brain, which are typically generated

in response to peripheral or external stimulations, and

appear as somatosensory, visual, and auditory brain

potentials, or as slowly evolving brain activity observed

before voluntary movements or during anticipation of

conditional stimulation [14]. The amplitude of ERPs ranges

from 1-30µV, lying relatively low in the background EEG

activity. Therefore, often there is a need for the use of signal

averaging procedure for their elucidation. The ERP

waveform can be quantitatively characterized across three

main dimensions: amplitude, latency, and scalp distribution.

The ERP signals are either positive such as P300 (positive

peak appearing 300ms after stimulus onset), or negative

such as N170 and N100 (negative peak appearing

170/100ms after stimulus onset). The digits indicate the time

in terms of milliseconds after the sensory stimuli (audio,

visual, or somatosensory).

Although determination of the location of the ERP

sources within the brain is a difficult task, the scalp

distribution of an ERP component can often provide very

useful and complementary information to that derived from

amplitude and latency. The scalp recorded ERP voltage

activity reflects the summation of both cortical and sub

cortical neural activity within each time window. On the

current source density (CSD) maps reflect primary cortical

surface activity. CSD maps are useful for forming

hypotheses about neural sources within the superficial

cortex.

2.4. Data processing

Analysis of electro encephalogram (EEG) data was

performed with MATLAB R2007a. The signal

corresponding to each visual stimulus was cut for averaging

purpose to get the event related component. Pre-stimulus

data of duration 200ms was considered for each epoch to

eliminate base line drift. Therefore, the total length of each

epoch is 200ms (pre-stimuli) and 800ms (stimuli onset),

which is shown in the figures.

2.4.1. ERP analysis

Following artifact removal, the EEG signal was down-

sampled to 256Hz. The time locked data were then filtered

with a 0.5-50Hz band pass filter. Epochs of -200ms before

stimulus onset and 800ms after stimulus onset were averaged

separately for faces and symmetrical objects.

3. RESULTS AND DISCUSSION

In the specific context of symmetric image-processing, an

ERP component is typically observed in the sensors at the

occipito-temporal region and referred to as the N200. It is

characterized by negativity about 200 milliseconds

following the onset of symmetric-images. The amplitude of

the N200 is significantly reduced for face images, as shown

in the figure below. A large body of work has supported the

idea that the N170 appears for a stimulus similar to a face in

appearance. However, the N200 appears to be sensitive for

symmetric objects. The N200 amplitude and latency were

measured at the maximum negative peak of the ERP for

faces and symmetric objects within 170-210ms.


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To assess the pattern of the N200 response for faces and

symmetric objects in parietal, temporal and occipital

electrodes (T5, O1, P3, O2, T6, P4), two ways ANOVA

with hemisphere and category (face, symmetric objects) as

repeated measures and amplitude or latency as dependent

measures were selected. Analysis reveals no difference

between the pattern of responses to faces and symmetric

objects at the right and left occipito-temporal electrodes.

The amplitude of the N200 was however larger for

symmetric novel objects than faces [F(1,9)=37.7, p<0.001].

The figures (3, 4) show the variation of amplitudes in

various electrodes.

The latency of the N200 for novel objects and faces

were very similar. A one-way ANOVA with hemisphere as

repeated measures revealed no difference in latency between

the components during two different objects condition

[F(1,9)=2.3, p=0.17].

The results of this study unveiled a number of

dissociations between different forms of neural activity

associated with early face and object processing. The

amplitude of the N200 was higher in response to non-face

components than in response to face components. This result

links the N200 to a symmetric object detection mechanism

with the facial recognition mechanism.

Fig 2: 10-20 system of EEG electrode placement

Fig 3: ERP component from left occipital (O1) region for

faces and unnatural symmetric objects

Fig 4: ERP component from right occipital (O2) region for

faces and unnatural symmetric objects

4. CONCLUSION

By using novel techniques, brain regions that were activated

by natural and unnatural symmetric objects were

distinguished. Consistent with previous studies, fusiform

gyrus (especially right and left FFA, O1 & O2) was the most

reliable region that was activated by faces and novel objects,

though the amplitudes of activation for faces were smaller

compared to objects. The results also provided some clue as

to the „domain general concept‟ of the human brain with

respect to similar types of visual inputs in „domain specific

regions‟. The activation level indicates the neuronal activity

during the recognition of novel objects, which are peculiar

in nature; hence the amplitudes of activation in such cases

are higher in comparison to faces, which are important and

common images recognized by the human visual system.

REFERENCES

[1] H.P. Beeck, J. Haushofer and N.G. Kanwisher, “Interpreting

fMRI data: maps, modules and dimensions,” Reviews, J.

Neuroscience,Nature, vol.9, pp. 123-135, 2008.

[2] R.Malach, et al., “Object-related activity revealed by functional

magnetic resonance imaging in human occipital cortex,”. Proc.

Natl. Acad. Sci. USA, vol.92, pp. 8135-8139, 1995.

[3] K.G. Spector, Z. Kourtzi and N. Kanwisher, “The lateral

occipital complex and it‟s role in object recognition,” Vision Res.,

vol.41, pp. 1409-1422, 2001.


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[4] N.Kanwisher, R.P. Woods, M. Lacoboni and J.C. Mazziotta,

“A locus in human extrastriate cortex for visual shape analysis,”

J.Cogn. Neurosci., vol.9, pp. 133-142, 1997.

[5] N.Kanwisher and G. Yovel, “The fusiform face area: a cortical

region specialized for the perception of faces,” Philos. Trans. R.

Soc. Lond. B Biol.Sci., v. 361, pp. 2109-2128, 2006.

[6] N.Kanwisher, J. McDermott and M.M. Chun, “The fusiform

face area: a module in human extrastriate cortex specialized for

face perception,” J.Neurosci., vol.17, pp. 4302-4311, 1997.

[7] K.G. Spector, “The neural basis of object perception,”

Curr.Opin. Neurobiol., vol. 13, pp. 159-166, 2003.

[8] B.Rossion et al., “A network of occipito-temporal face

sensitive areas besides the right middle fusiform gyrus is necessary

for normal face processing,”Brain, vol. 126, pp. 2381-2395, 2003.

[9] R. Epstein and N. Kanwisher, “A cortical representation of the

local visual environment,” Nature, vol. 392, pp. 598-601, 1998.

[10] M.V. Peelen and P.E. Downing, “The neural basis of visual

body perception,” Nature Rev. Neurosci., vol. 8, pp. 636-648,

2007.

[11] R.F. Schwarzlose, C.I. Baker and ] N.Kanwisher, “Separate

face and body selectivity on the fusiform gyrus,” J.Neurosci.,vol.

25, pp. 11055-11059, 2005.

[12] B.D.McCandliss,L.Cohen and S. Dehaene, “The visual word

form area: expertise for reading in the fusiform gyrus,” Trends

Cogn.Sci., vol.7, pp. 293-299, 2003.

[13] T.Allison, A. Puce, D.D. Spencer, G.McCarthy,

“Electrophysiological studies of human face perception: potentials

generated in occipitotemporal cortex by face and non-face stimuli,”

vol. 9, pp. 415-430, 1999.

[14] B. Blankertz, K.R. Muller, G. Curio and T.M. Vaughan, “The

BCI competition:progress and perspective in detection and

discrimination of EEG single trials,”IEEE Trans. Biomed. Engg.

Vol. 51, pp. 1044-1051, 2003.

CONFERENCE ON ―SIGNAL PROCESSING AND REAL TIME OPERATING SYSTEM (SPRTOS)‖ MARCH 26-27 2011

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Modelling Neuron for Biomedical Applications: A Review

1Taslima Ahmed , 2 Dr Jiten Ch Dutta

1Dept. of Electronics and Instrumentation Engg. IIMT College of Engineering, Greater Noida, Knowledge Park-III, UP-201306, India

2Dept of ECE, Tezpur University , Napaam Post, Tezpur, Assam – 784 028, India

[email protected]

Abstract: Modelling of neuron including the action of synapse has played an important role in the

field of biomedical engineering and neurology for simulation of receptor function and electrical

activity of the postsynaptic neuron. In this paper, we review some literatures concerning the

development of different neuron models giving special emphasis on Dutta and Roy models.

Keywords— Neuron, Synapse, MOSFET, Postsynaptic membrane.

Over past few years, many electronic circuits

have been developed to reproduce the behaviour of

nerve axons [1]-[5]. A very good account of this

type of modelling is reviewed by Harmon et al [6]

and Lewis [7]. But among these models,

neuroscientists have, so far, utilized Hodgkin-

Huxley (H-H) model as a circuit analog of the

axonal membrane. In this model, the capacitance of

the lipid bilayer of postsynaptic membrane is

represented by CM and is found to be constant and

the membrane resistance is determined in terms of

three parallel conductances gNa, gk, and g0 as shown

in Fig.1. The conductances gNa, gK, and go represent

the membrane permeability of Sodium, Potassium

and other ions respectively. ENa, and EK are

respectively the chemical potentials of Sodium and

Potassium i.e., Nernstian membrane potential for

Sodium and Potassium. EO is the resting potential.

The gk and gNa conductances are found to be time

and voltage dependent.

Fig.1: H-H model

The total current in this model is given by:

I = Im+Io-INa+IK (1)

If Vm be the postsynaptic membrane potential

established by the ionic and capacitive membrane

current then

I = C(dVm/dt)+gO(Vm–EO)–gNa(Vm–ENa)+gK

(Vm–EK) (2)

The equations (1) and (2) are called H-H equations

which are simple and capable of explaining the

activity of neuron with the help of variable

permeability of membrane for different ions, e.g.,

sodium, potassium and other ions. But this model

has not explained the function of synapses on which

the variable permeability of postsynaptic membrane

arises.Refering to the biological activities of neuron,

the primary mode of communication between two

neurons is a biochemical process that occurs at

synapse. Synapse is essentially a junction called

synaptic cleft between two neurons namely

presynaptic and postsynaptic neurons. Signal from

presynaptic neuron to postsynaptic neuron is

transmitted through neurotransmitters released by

presynaptic neuron terminals in to the synaptic cleft.

Neurotransmitters diffuse through the cleft and then

bind with the specific receptor sites of the

membrane of postsynaptic neuron. This binding

mechanism initiates the opening of transmitter


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gated ion channels resulting in to flow of ions into

the cell or out of the post synaptic cell.

The membrane of post synaptic neuron has two

types of ion channels – excitatory and inhibitory.

The excitatory channels are those which are specific

to sodium ions and inhibitory channels are those

which are specific to Chloride ions. The flow of

Sodium ions into the cell causes a membrane

potential called excitatory postsynaptic membrane

potential (EPSP) whereas the flow of Chloride ions

causes an inhibitory postsynaptic membrane

potential (IPSP).The electrical mechanism of

synapse is shown in Fig 2. When an action potential

from the presynaptic neuron arrives at its terminals

connecting the cleft, neurotransmitters are released

into the cleft which diffuse through the cleft and

bind with the receptor sites of the postsynaptic

membrane. This binding mechanism opens the ion

channels situated at the membrane surface and ions

move into or out of the membrane. If the synapse is

excitatory, Sodium ions flow into the cell resulting

into positive current. As a result the membrane

depolarizes. If sufficient number of Sodium

channels open, then membrane potential will be

greater than the threshold VT of the neuron and

initiates an action potential. If the synapse is

inhibitory, Chloride ions move into the cell,

resulting into negative current. As a result the

membrane hyperpolarizes. If the numbers of

opening of Chloride channels are sufficiently large

then membrane potential will be able to initiate an

action potential in negative direction. The

presynaptic equivalent circuit is shown in Fig 3(a),

where I is the total current from ionic channels of

all synapses and E1 ,E2, …., EM represent the

chemical potentials of each corresponding ions. For

example, EM may be ENa or may be ECl. The total

current I will stimulate the postsynaptic neuron to

initiate an action potential [8]. Fig 3(b) shows the

equivalent circuit of a synapse which is developed

by adding H-H equivalent circuit with the

presynaptic circuit shown in Fig 3(a) [9].The

postsynaptic membrane consists of a lipid bilayer

and transmembrane protein ion channels. Some ion

channels such as sodium, chloride etc. are

controlled by the neurotransmitters that bind with

the receptor sites, i.e. the

Fig.2: Electrical mechanism of synapse

Fig.3(a): Equivalent circuit of a presynaptic neuron

Fig. 3(b): Electrical equivalent circuit of synapse

amount of ionic current is dependent upon the

activity of the transmitter-receptor binding. In

simplest case, the binding reaction may be

represented as


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Neuro-transmitter+Receptor(Closed)1

2

K

K

Neuro-

transmitter –Receptor(Open) (3)

Where K1 and K2 are the forward and backward

rate constants respectively. The transmitter gated

channels, therefore, have variable conductance

dependence on the binding activity of transmitters.

Dutta and Roy therefore have modeled transmitter

gated ion channels by MOSFET, because MOSFET

functions as a voltage controlled conductance in its

linear region [10]. In this model they have

considered gate voltage as a time dependent voltage

given by

Vg(t)=V0[(1–exp(-k1t)+exp(-k2t)U(t-tm)]

(4)

Where K1 and K2 are time constants analogous to

the rate constants of equation (3), U(t-tm) is the

Heaviside function andVo is a voltage proportional

to the maximum attainable conductance, when all

the transmitter-gated channels for a specific ion are

open. Based on this, they have developed a

biologically motivated model as shown in Fig.4 (a).

Their circuit models both for excitatory and

inhibitory synapses are shown in Fig.4(b) and 4(c)

respectively. In both these models they have

divided the postsynaptic membrane into three

patches to represent spatial summation of the

sodium current and chlorine current controlled by

respectively sodium and chloride conductances [10].

Fig.4(a): Biologically motivated model of

postsynaptic membrane.

Fig.4(b): Circuit model for excitatory action of

synapse

Fig.4(c): Circuit model for inhibitory action of

synapse

The simulation results from this model [Fig.5]

indicate that this model can be used in neuro

bioengineering area for simulation of

neurotransmitter-receptor binding activity and

electrical activity of the postsynaptic neuron.


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Fig.5: Simulation results of excitatory and

inhibitory actions of postsynaptic Membrane. Top

waveform represents the EPSP and bottom

waveform represents the IPSP.

In an another model, they have used ion sensitive

field effect transistor (ISFET) as circuit analog and

incorporated into the famous Hodgkin-Huxley (H-H)

model of neuron to substitute the variable Na+ and

Cl- conductances, the details of which may be

obtained in reference [11]. They have mentioned

that such model has additional advantages over

MOSFET based model. The advantages that they

have high lighted are: (i) Measurement of different ions that diffuse through the post synaptic membrane and hence pH (ii) measurement of neurotransmitters diffused through the synaptic cleft by converting the ISFET into neurotransmitter sensitive enzyme modified FET (ENFET). This model, according to them, may become a useful research unit in neurology for biotelemetry applications. The second advantage that ENFET can also be used as circuit analog, in an effort, they have modeled it using ENFET sensitive to acetylcholine neurotransmitter for simulation of acetylcholine gated ion channels of the post synaptic membrane at the synaptic cleft[12].

MOSFET and ISFET based electrical models both

for excitatory and inhibitory actions of neurons

have been reviewed. It is concluded that ISFET

based models are more biologically motivated as

these are compatible with biological medium and

there is possibility of measurement of

neurotransmitters diffused through the synaptic

cleft by converting the ISFET into neurotransmitter

sensitive ENFET. These biologically motivated

models may become useful research and teaching

units in biomedical area in general and neurology in

particular.

REFERENCES [1] Hodgkin, A, L and Huxley, A. F., ―A

quantitative description of membrane current and its application to conduction and excitation in nerve‖, J. Physiol, 117. 500-544(1952)

[2] Hodgkin, A. L., ―Ionic movements and electrical activity in giant nerve fibers‖, Proceedings of the Royal Society of London. Series B, Biological Sciences, Vol. 148, 1-38(1957)

[3] Fitzhugh, R., ―Threshold and plateaus in the Hodgkin-Huxley nerve equations‖, J. Gen. Physiology, 43, 867-(1960)

[4] Johnson and Hanna, ―Membrane model: A single transistor analog of excitable membrane,‖ J. Theoret. Bio, 22, 401-411(1969)

[5] E.R. Lewis, ― Neuroelectric potentials derived from an extended version of the Hodgkin and Huxley model,‖ J. Theor. Biol. Vol.10,125-158, 1965

[6] L.D. Harmon and E.R.Lewis, ― Neural modelling”, Physiol. Rev. , Vol, 48, 513-591, 1966

[7] E.R.Lewis, ―Using electronic circuits to model simple neuroelectric interactions,‖ Proc.IEEE,vol 56, 931-949, June 1968.

[8 ] Xiao-lin Zhang, ―A Mathematical Model of a

Neuron with Synapses

based on Physiology‖, Nature Proceedings,

npre.2008.1703.1. March

2008.

[9] Soumik Roy, Jiten Ch Dutta, Shikhamoni

Phukan, ―Integrate-and-

Fire based Circuit model for simulation of

excitatory and inhibitory

synapses‖, Canadian Journal on Biomedical

Engineering & Technology

Vol. 1, No. 2 March 2010, 49-51. [10] Jiten Ch Dutta and Soumik Roy

―Biologically motivated Circuit model for simulation of excitatory and inhibitory synapses‖, Canadian Journal on Biomedical Engineering & Technology Vol. 1, No. 2 June 2010, 49-51.

[11] Jiten Ch Dutta and Soumik Roy, ―An Electronic Circuit Model for simulation of Synaptic Communication: The NEUROISFET for Wireless Biotelemetry‖, accepted for publication in the IEEE conf. on wireless communication, 24-25 Feb, 2011, BITS, MESRA

[12] Jiten Ch Dutta and Soumik Roy ―Biologically inspired Circuit model for simulation of Acetylcholine gated ion channels of the Postsynaptic membrane at synaptic cleft‖,


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Proc. 2010 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES2010, Nov 30–Dec’ 02, 2010


COS0103

Organ Failure Assessment in Malarial Patients

Using Artificial neural networks

Mr. Jay Kumar Pandey

Shri Ramswaroop Memorial College of Engineering and Management , Lucknow , U.P

Email id : [email protected]

Abstract

Health Care Management is one of the most important

and most important research areas of the new

millennium. The main purpose of this work was to analyze

the data on malaria patients in India using the artificial

neural networks such as Brain maker and statistical

analysis software (SAS). This data is known that SOFA

(Sequential Organ Failure Assessment) score and this

information useful in providing the condition of the organ

and based on this, the patient’s survival rate can be

estimated. In this analysis, the same individual SOFA

score of different organ systems (esp. the ones which are

used to calculate the overall SOFA score) for 753 patients

admitted in an hospital in India with malaria were trained

using artificial neural networks to provide better

predictions of the survival rate compared to the overall

SOFA scores. Using the statistical analysis tools like SAS,

the statistical aspects of this data was studied. Also, using

SAS, analysis was done on the data of the Indian malaria

patients and the interested outcomes were projected in the

figures at the end of this paper. The results show that the

artificial neural network turns out to be an efficient

predictor of survival rate and the results were

comparable to the SOFA scoring system where the same

variables used for calculating the overall SOFA score

were used for training the neural network

Keywords: Artificial neural networks, SOFA, malaria,

organ failure, statistical analysis

1. Introduction

Currently available prediction models such as the Acute

Physiology and Chronic Health Evaluation APACHE) II,

Simplified Acute Physiology Score (SAPS), and

Mortality Probability Models (MPM) use values taken

within the first 24 hrs of an ICU stay. However, these

scores ignore the many factors that can influence patient

outcome during the course of an ICU stay beyond the first

24 h. The Sequential Organ Failure Assessment (SOFA)

assesses patients for organ dysfunction not only at ICU

admission but serially APACHE II, SAPS, MPM etc,

researchers never stop working on during the ICU stay

and was first developed to evaluate morbidity.

Although this scoring system was developed to describe

and quantify organ function and not to predict outcome,

the obvious relationship between organ dysfunction and

mortality has been demonstrated in several studies. These

SOFA scores do not predict the exact rate of morbidity

every time but provide meaningful result most of the

times. Nowadays although we have many scoring systems

like creating a better system compared to the other which

always happens in a continually developing field such as

medicine. This work was done in an attempt to find such

system which is efficient compared to the standard system

like SOFA score.

Determining the patients survival chance based on these

scoring systems is very critical in an Indian hospital

environment. In this study two different methods of

predicting the hospital mortality were reviewed. One was

using these SOFA scores, doing the statistical analysis on

the available data to see how accurate are these scores in

predicting the mortality while the other one is using the

neural networks to predict the mortality rate. Later these

two results were compared to see which method is better

in predicting the mortality.

This work was done in an attempt to develop an

efficient method compared to the standard method. It was

observed that the neural networks were as efficient as the

traditional SOFA scoring system in predicting the

mortality of the patients.

1.1. Severe Malaria

Unless P. falciparum malaria is promptly diagnosed and

treated, the clinical picture may deteriorate rapidly. There

is significant morbidity and mortality associated with the

occurrence of severe malaria. Young children, pregnant

women, immunosupressed patients and any non-immune

persons are at risk for the development of complications.

One can assume that all South Africans living in the

malaria areas in this country and all South African

travelers are non-immune.


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1.2. Indicating Features

1.2.1. Clinical Features

Impaired consciousness, convulsions, Respiratory

distress: acidosis, ARDS, pulmonary edema, Jaundice,

Bleeding and Shock.

1.2.2. Biochemical Features

Renal impairment – serum creatinine, >265 μmol/liter or

rapidly rising creatinine or urine output

>265 <400 ml/day (adult)

• Acidosis (plasma bicarbonate <15 mmol/liter)

(serum lactate > 5 mmol/liter)

• Hepatic impairment

(transaminases > 3 times normal)

• Hypoglycemia (blood glucose < 2.2 mmol/liter)

• Hypoxia (pO2 - < 8 Kpa in room air)

1.2.3.Haematological Features

• Parasitaemia > 5%, or > 3+

• Haemoglobin < 6 g/l or haematocrit<20%

• > 5% neutrophils contain malaria pigment

• Presence of schizonts of P. falciparum in peripheral

blood smears

• Evidence of DIC

Figure.1. A.Cellular events that occur late in disease

development and contribute to malaria pathogenesis.

The early events of innate immune-cell and platelet

activation by parasite antigens, and the production of

cytokines, chemokines and vaso active molecules are not

depicted. P RBCs adhere through e CAMS

(i) Pselectinis expressed at low levels in ECs that

have not been activated, indicating that e CAMs with high

basal levels, such as ICAM1, and CD36 function in early

adhesion. This cell adhesion, together with molecules

derived from immune cells and platelets, activates ECs to

increase their e CAM levels, increasing p RBC adhesion

(i) and facilitating the adhesion of platelets

(ii) and leukocytes

(iii) All cell types use similar processes and cell-

adhesion molecules to adhere to the endothelium (i–iii).

They can also form rosettes in the blood, which are

analogous to mini-thrombi in sepsis. Cell adhesion and

vasoactive molecules released during infection cause

disruption of the endothelial tight junction

(iv) and, potentially, leakage of plasma proteins into

the interstitial space

(v). whether this leak becomes greater, or ECs

become apoptotic and slough off, leaving gaps for

endothelial hemorrhage

(vi), has yet to be determined. The blockade of

functional capillaries leads to a build up of lactic acid in

the tissue

(vii) but only mild hypoxia because the blood flow in

the arterioles is maintained during ECM.

Figure.1.B. Annex in V-stained endothelial cells

incubated with neutrophils plus serum from a patient with

fatal P. falciparum malaria (green fluorescence). Necrotic

cells were stained with propidium iodide (red

fluorescence). The inset shows endothelial cells incubated

with serum from a healthy person as a negative control.

Figure.1.B

Figure.1.C.

Figure.1.(C) TUNEL-stained kidney (a) and lung (b)

sections from two patients who had died from fatal P.

falciparum malaria showed the highest apoptosis rates


COS0103

(median of 61% and range of 54 to 68 for two patients),

followed by sera from patients with severe P. falciparum,

mild P. falciparum, and P. vivax malaria. Sera from

Patients with fatal malaria plus neutrophils induced large

efects in the endothelial cell layers, a phenomenon that is

consistently seen secondary to apoptosis.

2. Observed Organ Failure Most of the patients with malaria have a pre-existing

organ dysfunction or failure or develop organ failure

during the progress of the disease sometimes which may

lead to death of the patient. Organ dysfunction is

associated with high rates of hospital morbidity and

mortality and as such accounts for a high portion of ICU

budget. Recently developed organ failure scores such as

Sequential organ failure Assessment (SOFA) can help

assess organ dysfunction or failure over time and are

useful to evaluate Morbidity.

Malaria occurs in 300–500 million individuals annually,

resulting in 1.5–2.7 million deaths. Most deaths occur due

to Plasmodium falciparum infection, which produces life

threatening cerebral, renal, hepatic, and hematological

dysfunction in about 1% of cases. This causes infected red

blood cells (RBCs) to adhere to capillary and venular

endothelium, non infected erythrocytes, and platelets,

resulting in circulatory obstruction. The brain is the most

common organ to be involved in severe malaria. Up to

45% of cerebral capillaries may be occluded at

postmortem examination because the receptors to which

infected RBCs adhere are maximally expressed on

cerebral capillary endothelium. Coma also may result

from interference with synaptic transmission by nitric

oxide from vascular endothelium, raised intracranial

pressure due to vasodilatation, and increased capillary

permeability. A combination of microcirculatory

occlusion, cytokine activation, and nitric oxide-mediated

changes in vascular tone are believed to cause organ

dysfunction that characterizes severe malaria. However,

malaria is not well recognized in critical care literature as

a cause of multiple organ dysfunction syndromes.

3. Neural Network based Model

Artificial neural networks are computational paradigms

based on mathematical models that unlike traditional

computing have a structure and operation that resembles

that of the mammal brain. Neural networks lack

centralized control in the classical sense, since all the

interconnected processing elements change or “adapt”

simultaneously with the flow of information and adaptive

rules. The commonest learning mechanism in artificial

neural networks is the back-propagation algorithm,

wherein the system predicts the outcome for each patient

based on past experience (memory) and compares this

with actual outcome.

The advantage of neural networks over conventional

programming lies in their ability to solve problems that do

not have an algorithmic solution or the available solution

is too complex to be found. Neural networks have been

applied within the medical domain for clinical diagnosis,

image analysis and interpretation, signal analysis and

interpretation, and drug development.

4. Data Analysis and Knowledge Discovery

The development phase of the neural network began with

the conception of a study protocol which involved the

comparison of the morbidity rates predicted by APACHE

II and Artificial neural networks. Several models of

artificial intelligence techniques have been used in the

ICU. One such technique suited to predict mortality is the

artificial neural network.

The main goal of this paper is to compare neural

networks with an already validated and commonly used

outcome prediction model. The question remains whether

a model such as SOFA scoring is better at predicting

hospital outcome than a model derived from Indian

patients treated in an Indian hospital. It was therefore

attempted to compare the predictive accuracy of artificial

neural networks derived from Indian patients with the

SOFA scoring system.

The variables which were used to train the neural

network were similar to the ones used for predicting the

overall SOFA score. The sofa scores used were those of

the six different organ systems estimated during the first

48hrs of the patient’s admission in the hospital.

The network training was done using Brain maker

professional and Net maker Professional. The data

which has been worked on was that of 753 patients who

were suffering from malaria in an Indian hospital. Out of

this, data on 553 patients was used to train the network

and that of 200 patients was used to test the network. The

results were then read in to a text file.

Using SAS (Statistical Analysis Software), the

frequency bar charts was drawn for survival and SOFA

scores. The SOFA Scores were then recoded to six

different ranges and the table analysis was done with

these ranges and survival. This table analysis gives the

percentage of patient’s corresponding to each different

SOFA range. The correlation procedure was done to find

the correlation between SOFA score and survival.

Using Excel 2002, graphs were drawn for the output

acquired from neural network to see the percentage of

people survived and also graphs were drawn on these 200

patients used for testing the neural network to see the

actual percentage of people survived. Then both of these

results were compared to see predictive capability of the

trained network.

Using Access 2002, queries were written to compare the

real survival rate with that of the output acquired from


COS0103

neural networks. Also data was queried to find if the OFA

score ranges actually predict the mortality rate

comparable to that of the real data. Then those data of

patients whose SOFA range is not consistent with the

survival/mortality rate were compared to the prediction

done by ANN’s and it was found that the survival rate for

these patients was predicted accurately by ANN’s.

Figure .2.A. Sequential Organ Failure Assessment Data

5. Experimental Results Using Neural Networks:

After training the data using Brain maker, the least

RMS error and Average errors were determined and the

plot of the output is shown in the figure 2.

Some of the data were used to validate the model. The

trained model behaved very well and the accuracy of the

model was over 88%. It was found that the performance

of the model of neural networks was significantly better

than that of the SOFA score model when applied to the

given data set.

The SOFA range 4 had the patients who both survived

as well as died which explains that SOFA scoring system

also has its inadequacies. When this SOFA range (SOFA

range=4) was further analyzed using Microsoft Access

2002 to check the individual values present in this range,

it was observed that 3 patients survived and 3 patients

died. Surprisingly, it was also observed that the 3 patients

out of which 2 people survived and 1 died had the same

SOFA score of 16. This doesn’t explain the fact how 2

persons with the same SOFA score can show distinct

Survival capabilities. When these 6 patients who had the

SOFA range of 4 was compared to the results predicted

by Brain maker, it was observed that the neural network

(Brain maker) gave the correct prediction rates for these

patients.

The correlation procedure was performed on the test

data (200 patients) on SOFAMAX and the SURVIVAL.

It was observed that the parameters have very good

correlation as seen in figure3.

These SOFAMAX values were then recoded in to

ranges to better predict the variations in survival for

different SOFA score ranges. The table analysis of these

SOFAMAX ranges with SURVIVAL was done indicating

the percent of patients in each SOFAMAX range as

shown in figure 4.

Figure 2 .B. Statistical analyses of the data using SAS:

Figure 3: Correlation Procedure

Figure 4: Data analysis of SURVIVAL and

SOFAMAX ranges The histograms for these SOFAMAX ranges was

plotted for both survivors and non-survivors indicating

the total number of survivors/non survivors for each

SOFAMAX range as shown in figure 5. The frequency

plot of the non-survivors with different SOFA score


COS0103

ranges is supposed to show that the percentages of people

with higher SOFA score are supposed to have higher rate

of mortality.

Figure 5: Bar Chart of survivors and non-survivors

for different SOFAMAX ranges

But it’s observed that there is a decline in the graph

level which suggests that the rate of mortality is lesser in

the SOFA range-6 compared to the Percentage of

mortality with SOFA range 5. This percentage decline

with increase in SOFA range leads to an ambiguous

condition. When results of the tested network were

checked with the original data, it was observed that the

trained network produced accurate results for 198 patients

out of these 200 patients, which shows that it gave 99% of

the exact results. The reason for being so accurate in

prediction might have the possibility that the data used for

training and testing is very low. Most of the times in the

real world situations the data sets may be so large that the

neural network is not trained as good as it was in this case

and also the results may not be as accurate as it was in

this case.

6. Conclusion

This project was aimed at making the peoples life in

Medical field (like physicians, nurses) easier while

assisting by providing them with tools which have been

created with the improving technology to make the

complex decisions easier. Such systems not only help

them to predict the outcomes but are much efficient in

cutting costs of the hospital due to early discovery of this

knowledge. This project also aims at such embedded

technology into the field of medicine to make the

complex decision easier and foresee the outcome most of

the times.

This work has been successful in achieving its

objective. The results are reliable. Based on the results

above still it can be said that it is an efficient neural

network which works as good as the standard SOFA

scores and is also little efficient compared to the standard

SOFA scoring system here.

7. References [1] http://www.malaria.org/

[2] http://www.ncbi.nlm.nih.gov/entrez/query.fcgi

[3] http://www.cdc.gov/malaria/

[4] http://my.webmd.com

[5] http://home.uchicago.edu/~junji/KRL_HP/chestrad.htm

[6] www.nd.com/whatisnn.htm

[7] http://www.openclinical.org/home.html

[8] “Intelligence in medical image processing” Image and

Vision Computing, 19(4), 177.

[9] Dinesh Mital, Shankar Srinivasan, Syed Haque and Radhika

Uppalapati Dept. of Health Informatics Univ. of Medicine and

Dentistry of New Jersey School of Health Related Professions

Newark, NJ.

[10] Aizenberg I., Aizenberga N, Hiltnerb J et al (2001).

“Cellular neural networks and computational intelligence

inmedical image processing.” Image and Vision Computing,

19(4), 177-183.

[11] Anthony, D., Hines, E., Barham, J., & Taylor, D. (1990).

“A Comparison of Image Compression by Neural Networks and

Principal Component Analysis”. In International Joint

Conference on Neural Networks, Vol. 1, pp. 339--344.

[12] Baxt, W. G. (1995). “Application of Artificial Neural

Networks to Clinical Medicine”. Lancet, 346, 1135-1138.

[13] “Review of Neural Network Applications in Medical

Imaging and Signal Processing.” Medical and Biological

Engineering and Computing, 30(5), 449-464.

[14] Miller, A. (1993). “The Application of Neural Networks to

Imaging and Signal Processing in Astronomy and Medicine”

Ph.D. thesis, Faculty of Science, Department of Physics,

University of Southampton.

[15] Weinstein, J., Kohn, K., Grever, M., et al. (1992). “Neural

Computing in Cancer Drug Development: Predicting

Mechanism of Action”. Science, 258, 447--451.

[16] Weinstein, J., Kohn, K., Grever, M., et al. (1992). “Neural

Computing in Cancer Drug Development: Predicting

Mechanism of Action”. Science, 258, 447--451.

pp. 3546--3551.

http://www.malaria.org/

http://www.ncbi.nlm.nih.gov/entrez/query.fcgi

http://www.cdc.gov/malaria/

http://my.webmd.com/

http://home.uchicago.edu/~junji/KRL_HP/chestrad.htm

http://www.nd.com/whatisnn.htm

http://www.openclinical.org/home.html


COS0201-1

AN INTELLIGENT CONTROL METHOD TO REDUCE BRAKE NOISE

Pramod Kumar Pandey

[email protected]

Department of Electrical Instrumentation

Engineering Thapar University Patiala

Punjab -147004

Yaduvir Singh

[email protected]

Department of Electrical Instrumentation

Engineering Thapar University Patiala

Punjab -147004

Abstract: In this paper, a novel approach

to suppress vibration that causes brake

noise is proposed employing a closed-loop

feedback control method using an active

force control (AFC) based strategy and

fuzzy logic controller. The idea is to

introduce an active element that

dynamically compensates the

disturbances through a control

mechanism that takes into account the

direct measurements and estimation of

parameters in the AFC section. A disc

brake model is considered and simulated

taking into account a number of

operating and loading conditions. Results

clearly show the superiority of the

proposed AFC + fuzzy -based scheme

compared to the AFC+ PID and pure PID

counterpart in suppressing the vibration

and hence the brake noise.

Key Terms - Brake noise; vibration;

robust; active force control, PID, Fuzzy

logic controller.

I. INTRODUCTION

The brake system of an automobile

typically consists of the contact metallic

solids rubbing against each other, which

frequently generates undesirable noise and

vibrations. Thus, noise generation and

suppression have become an important

factor to be considered in the design and

manufacture of brake components. Indeed,

as note by Abendroth and Wernitz [1], a

large number of manufacturers of brake pad

materials spend up to 50% of their

engineering costs on issues related to noise,

vibration and harshness. Even to this day,

there is no precise or conclusive definition

of brake squeal that has gained complete

acceptance. It is also worth mentioning that

since in a vehicle with disc brakes installed

at the front wheels while drum brakes at the

back wheels, around 70% of the braking

action occurs at the front wheels. Thus, it is

expected that most of the noise and squeal is

coming from the front disc brake system.

In literature, there are three major

methods to study and reduce brake squeal,

namely through mathematical modeling,

experimental and finite element methods.

One of the most recent works that has been

carried out for reducing brake noise using

finite element (FE) can be found in [3].

They developed a dynamic FE model of the

brake system, and based on their analysis,

the pad design changes can be made in the

FE model to determine the potential

improvements in the dynamic stability of the

system and also in noise reduction. Wagner

et al. proposed a new mathematical rotor

based model of a brake system that is

suitable for noise analysis [4]. Brief

descriptions of the previous mathematical

models that have been developed by other

researchers were explicitly outlined in their

study. Besides, there is also an active control

method known as dither control which

makes use of high frequency disturbance

mailto:[email protected]



COS0201-2

signal for the suppression of the automotive

disc brake squeal. Through this scheme, the

dither signal stabilizes friction induced self-

oscillations in the disc brake using a

harmonic vibration, with a frequency higher

than the squeal frequency generated from a

stack of piezoelectric elements placed in the

caliper piston of the brake system. The

resulting control vibration was not heard

from the brake system if an ultrasonic

control signal was activated. This system

assumes an open loop control mode in

which there is no requirement to detect the

presence of squeal and is much simpler in

design than the feedback control [5]. This

paper presents a closed loop control

employing system Active Force Control

(AFC) with PID element applied to a brake

model described in [6] in order to suppress

the brake noise and squeal. The main

advantage of the AFC technique is its ability

to reject disturbances that are applied to the

system through appropriate manipulation of

the selected parameters.

In addition, the technique requires much

less computational burden and has been

successfully demonstrated to be readily

implemented in real-time. AFC as first

proposed by Hewitt and Burdess [7] is very

robust and effective in controlling a robot

arm. Mailah [8, 9] has successfully

demonstrated the application of the

technique to include many other dynamical

systems with the incorporation of artificial

intelligence (AI) methods.

In this paper fuzzy logic+AFC based

model shows the better result than PID and

PID+AFC model. It also provides better

control accuracy and fast speed of response.

II. THE BRAKE MODEL

A disc brake system assuming a two

degree of freedom model based on the one

described in [6] is considered in the study.

The model consists of a conveyor belt with

constant velocity vB that is pushed with a

constant normal force FN against a block

modeled as a block of mass m. Fig. 1 shows

that the model is just a single-point mass

sliding over a conveyor belt and there are

two linear springs k1 and k2 parallel and

normal to the belt surface with the latter

regarded as the physical contact stiffness

between the objects in relative sliding

motion. In addition, there is another linear

spring k mounted at oblique angle of 45°

constituting the off-diagonal terms in the

model‟s stiffness matrix. For the friction

component, a coulomb model is assumed

such that FT = μ FN, where μ is the

coefficient of kinetic friction usually taken

to be constant. FN is a normal force and

since the normal force at the friction

interface is linearly related to the vertical

displacement x2 of the mass then the

resulting friction will become FF = μ k2 x2.

Assuming that the mass of the conveyor belt

system is larger than the mass block, it

implies that the vibration of the belt does not

show any changes due to its inertia.

The matrix form of the equation of motion

can be expressed as [6]

1 1 1 2 2

2 2 2

1 1 1

2 2 2

0 / 2 / 20

0 / 2 / 20

x x x k x

x x x N

c k k km

c k k km

----------------- (1)


COS0201-3

Figure 1.Two DOF model of a disc brake

system

III. CONTROL STRATEGY

After acquiring the model of the disc

brake and its related equation of motion, it is

required to control the vibration of the mass

with respect to the vertical direction, x2

considering an actuator that produces a force

F in parallel with the preload force N. Thus,

(1) will be written as

1 1 1 2 2

2 2 2

1 1 1

2 2 2

0 / 2 / 20

0 / 2 / 20

x x x k x

x x x N F

c k k km

c k k km

-------------------- (2) A robust control strategy is proposed

here employing an Active Force Control

(AFC) based scheme that is used in

conjunction with the conventional PID

controller. The PID controller was first

tuned with Ziegler-Nichol‟s method for

good performance and later the AFC part

was incorporated into the system to provide

the compensation of the disturbances. Fig. 2

shows the AFC scheme applied to a

dynamic translation system (disc brake).

AFC scheme is shown to be very effective

provided the actuated force and body

acceleration are accurately measured and at

the same time the estimated mass property

approximated [7, 8]. The essential AFC

equation can be related to the computation

of the estimated disturbance Fd as follows:

Fd= F- M’. a ------------- (3)

Where F is the measured actuating force,

M’ is the estimated mass and a is the

measured linear acceleration. This parameter

is then fed back through a suitable inverse

transfer function of the actuator to be

summed up with the PID control signal. The

theoretical analysis including stability of the

proposed AFC method has been sufficiently

described in [10]. A number of methods to

estimate the mass have been proposed in

previous studies such as through the use of

artificial intelligence (AI) and crude

approximation techniques [7-9]. In this

study, the use of crude approximation

method to approximate the estimated mass is

deemed sufficient. The main challenge of

the AFC method is to acquire appropriate

estimation of the mass needed to compute

the disturbance Fd in the feedback loop. A

conventional PID that is used with the AFC

scheme can be typically represented by the

following equation:

Gc(s) = Kp + Ki /s + Kd s -------------- (4)

Where Kp, Ki and Kd are the proportional,

integral and derivative gains respectively.


COS0201-4

Figure 2 Schematic diagram of AFC

strategy

IV. SIMULATION

The actuator is assumed to be a linear

type with a suitable constant gain. It

provides the necessary external energy to

suppress vibration in the model. The

parameters used in this study were taken

from the previous research [11]. However,

some of them need to be modified to suit the

application in the simulation. The detailed

parameters are as follows:

Minimal disc brake model parameters:

Body mass, m = 0.7 kg,

Spring stiffness, k = 10 N/m, k1 =

11 N/m, k2 = 20 N/m

Damping coefficient, c1 = 0.4 Ns/m,

c2 = 0.4 Ns/m

Friction coefficient, μ = 0.3

Normal preload, N = 5 N

Actuator:

Actuator gain, Q = 0.5

Reference value:

Reference input = 0.00 m (i.e. no

vibration)

Disturbance:

Magnitude of step function = 1 N

Frequency = 1.5 Hz

In this work, namely the step

disturbances are deliberately introduced to

the disc brake system to evaluate the

robustness of the system. The Simulink

diagram of the passive disc brake system

model is shown in Fig. 3. The schematic

block diagram was constructed from (1).

Figure 3 Passive brake system

In order to have an active disc brake

system, an actuator force for compensating

the disturbance force is required, and the

actuator force is controlled by a PID

controller which typically involves a

negative feedback loop. Hence, there are

two inputs to the dynamic disc brake system

which is the step disturbance and the

actuator force input. Figure 4 shows the

active disc brake system.

Figure 4 PID active brake control scheme


COS0201-5

To achieve better overall performance

of the brake system, AFC is „added‟ to the

PID controller, The AFC Simulink blocks

includes the estimated mass, parameter 1/Q

and the percentage of AFC gain. The input

to the AFC control is the mass acceleration

and the output is summed with the PID

controller output and then multiplied with

the actuator gain which finally generated the

actuator force. In order to get the effective

results using this method, it is required to

have a suitable mass estimation combined

with the best tuning of the PID controller

gains. Also a memory block is used to

eliminate the algebraic loops and algebraic

variable problems. Figure 5 shows an AFC

scheme that has a step input as its

disturbance.

Figure 5 PID+ AFC model for brake control

To tune the PID controller, we use the

Ziegler-Nichol‟s method and the results are

tabulated as shown in Table-1 TABLE 1 PID PARAMETERS TUNED

USING ZIEGLER-NICHOL‟S METHOD

PID Kp Ki Kd Ti Td

Gain 0.6 0.857 0.105 0.7 0.17

The estimated mass for the AFC loop was

obtained by trial and error (crude

approximation method) in which a suitable

value was easily found to be 1.4 kg, and the

percentage of AFC used is 100% implying

that the AFC loop employs full AFC

implementation.

Model for AFC+Fuzzy

FLC provides an algorithm which can

convert the linguistic control strategy based

on expert knowledge into an automatic

control strategy. Experience shows that the

FLC yields results superior to those obtained

by conventional control algorithms. In

particular, the methodology of the FLC

appears very useful when the processes are

too complex for analysis by conventional

quantitative techniques or when the

available sources of information are

interpreted qualitatively, inexactly, or

uncertainly. Thus fuzzy logic control may be

viewed as a step toward a rapprochement

between conventional precise mathematical

control and human-like decision making.

In this research paper a fuzzy logic

controller is developed. The inputs to the

FLC are applied force and change in force.

The output is change in vibration.

Figure 6: Membership function for input-1

Figure 6 shows the membership

function for force.

Figure 7: Membership function for input-2


function for change in force.


COS0201-6

Figure 8: Membership function for output


function for change in vibration.

Figure 9: The rule viewer of fuzzy logic

controllers

Figure 9 shows the rules of fuzzy

logic controller.

Figure 10: 3-D surface view of fuzzy logic

controller

Figure 10 shows the three dimensional

surface view of input and output

membership functions of fuzzy logic

controller. Figure 11 shows the fuzzy based

control of vibration.

Figure 11: Fuzzy+AFC model for break

control

V.RESULTS AND DISCUSSION

After tuning the PID control system

and obtaining suitable values for other

relevant parameters, the simulation was

executed for a period of 5 s since it is usual

that the brake process does not take more

than that duration. At first, the step

disturbance is applied and then the

simulation was performed without using

AFC and only the pure PID controller was

applied. The result of this process can be

seen in Figure 12. It can be observed that the

vibration that may result in producing noise

is relatively high (a peak of more than 0.6 m

in amplitude in some regions). Next the

simulation was carried out again but this

time considering 100% AFC mode plus the

PID controller.

Figure 12: Response of the brake model

with a step input disturbance for system with

PID


COS0201-7

Figure 13: Response in frequency domain

for PID

The result of the scheme is

superimposed onto previous graph obtained

as shown in Figure 11. It is evident that the

vibration is virtually reduced to a very low

value, implying that squealing can be almost

if not totally avoided. A closed-up view of

the graphical result of the PID+AFC scheme

is depicted in Figure 14. It clearly shows the

superiority of the scheme in rejecting the

vibration of the system. The maximum

amplitude of the vibration was less than

0.0004 m, which is very much lower

compared to the brake system operated with

a PID only controller.

Figure 14 Performances of PID+AFC

Controller

Figure 15: Amplitude vs. time plot using

step disturbance in AFC+Fuzzy

Figure 15 clearly shows the

superiority of the Fuzzy+AFC scheme in

rejecting the vibration of the system. The

maximum amplitude of the vibration was

much lower compared PID+AFC model.

Figure 16: Response of the brake model

with a step input disturbance for system with

AFC+Fuzzy


COS0201-8

Figure 17: Response in frequency domain

for AFC+Fuzzy

VI. CONCLUSION

A novel AFC-based scheme has been

proposed to suppress the vibration and noise

(squeal) emanating from a disc brake

system. Based on the simulation results, it is

obvious that when a pure PID controller is

applied to the brake system, vibration and

noise are in fact reduced but still a

noticeable amount of them remain.

However, upon applying the AFC+PID the

vibration and noise (squeal) are significantly

reduced and approaching zero datum.

Fuzzy+AFC strategy is robust and effective

in countering the undesirable effects. Future

works may include using neuro-fuzzy

algorithm with AFC model.

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integrated test concept: dynovehicle,

performance-noise”, Technical Report 2000-01-

2774, SAE, Warrndale, PA, 2000.

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Papadopoulos, “Review: Automotive disc brake

squeal”, Journal of Sound and Vibration, vol.

267, pp. 105-166, 2003.

[3] Yi Dai, Teik C. Lim, “Suppression of brake

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[4] Utz von Wagner, Daniel Hochlenert and

Peter Hagedorn “Minimal Models for Disk

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Georgia Institute of Technology, 2000.

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control”, Trans. on Mechanism and Machine

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[8] M. Mailah, “Intelligent active force control

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[10] J.S. Burdess, J.R. Hewit “An active method

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400, 1986.

[11] N. Hoffmann, M. Fischer, R. Allgaier, and

L., Gaul, “A minimal model for studying

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2002.

[12] S.M Hashemi-Dehkordi, M. Mailah and

A.R. Abu-Bakar “A Robust Active Control

Method to Reduce Brake Noise” Proceedings of

the 2008 IEEE International Conference on

Robotics and Biomimetics Bangkok, Thailand,

2009 978-1-4244-2679-9/08/ ©2008 IEEE.


COS0202-1

APPLICATION OF FUZY LOGIC IN AUTOMATIC BOTTLE FILLING SYSTEM

Nikita Agarwal1, Ritika Srivastava2, Preeti Dhiman3

EIE Department, Galgotias college of Engineering And Technology, Greater Noida

Abstract : In this paper we described the realization of an automatic Bottle Filling system designed with Fuzzy logic and implemented with microcontroller and matlab program .Previously the system was semiautomatic ,the major downfall of this approach was that system was not able to impose correct pressure inside the bottle ,so there were often Jamming condition. The fuzzy Logic method overcomes these shortcomings by deploying a set of heuristic rules and procedures derived from the deep knowledge obtained from experienced skilful control engineers. In this method we uses the Proximity sensor and Light dependent resistor to detect the presence of bottle and level of liquid inside it and Fuzzy control is applied to regulate the level of liquid inside the bottle.

Keywords: Fuzzy Logic, Heuristic rules, Fuzzy control, automatic bottle filling production, matlab, proximity sensor.

I. INTRODUCTION

Automatic Bottle Filling systems are used on large scale in Industries such as mineral water, soft drinks, food beverages and other similar industries. According to the International bottled water association (IBWA), sale of Bottled water has been tremendously increased by 500 percent over the last decade [1].The bottling procedure has been automated from a long time. Here we developed a proximity sensor using a infrared sensor that detects the presence of bottle and light dependent resistor and comparator is employed to sense the level of liquid in bottle.

Nikita Agarwal1, B.tech (Final year) , is with the Electronics and Instrumentation department, Galgotias College Of Engineering and Technology ,Greater Noida ,India (corresponding author to provide phone: 9953197920; email: [email protected])

Ritika Srivastava2 , Btech( Final year) , is with the Electronics and Instrumentation Department ,Galgotias college of Engineering and Technology ,Greater Noida ,India(corresponding author to provide phone: 9452829482 ; email: [email protected]).

Preeti Dhiman3 is working as Assistant Professor in EIE Department, GCET, Greater Noida, India(email:[email protected], phone: 9911923434)

Fuzzy logic methodology is applied to regulate the level of liquid. Microcontroller provides the sample input to the computer and with these input Matlab program generates control signal for solenoid tab. We apply Fuzzy principles to a real plant , to see if an automated fuzzy could improve the performance of plant, avoiding bottle to get stuck .The outline of bottle Filling system is shown in figure 1.

Fig 1. Outline of bottle filling system Courtesy: OMRON Tech for Focused Automation [2]

Fig2.Over and under filled Bottle Courtesy: OMRON Tech for Focused automation [2]

II. LITERATURE REVIEW

The semiautomatic system is not able to impose the right pressure level inside the different section of frame to maintain a constant velocity of the bottles along the production line, which often resulted in jamming condition. In [3] E.Mainardi used fuzzy logic for realization of air transportation bottling plant controller. The use of Fuzzy logic for


COS0202-2

the design and control of production system is widely used and recognized by scientists. Some studies described recommendation for effective application of Fuzzy Logic in production system. Fuzzy approach is also used in part family formation [4] , control of an industrial desktop robot which are used in different industrial application such as dispensing , soldering , screw tightening[5]. Recently fuzzy logic controller is employed in developing modular and reconfigurable robots (MRR) in flexible automation to reduce labour and increase throughput [6].Our study will focus on analyzing a fully automatic bottle filling system with respect to performance measures, equipment failures and breakdown.

III BRIEF DESCRIPTION OF THE SYSTEM

The system under study is a fully automatic bottle filling system. It consists of a sensor to detect the level of the liquid, RS232 and MAX232 is used for interfacing of microcontroller with computers, microcontroller to detect and transfer the signal and finally it is implemented using fuzzy logic and matlab programs. The system layout is shown in figure 3.

Fig 3. System layout.

A.AT89S52

The 89S52 is a low-power, high-performance CMOS 8-bit microcontroller with 8K bytes of in-system programmable Flash memory. The device is manufactured using Atmel’s high-density non-volatile memory technology and is compatible with the industry-standard 80C51 instruction set and pinout. The on-chip Flash allows the program memory to be reprogrammed in-system or by a conventional non-volatile memory programmer. By combining a versatile 8-bit CPU with in-system programmable Flash on a monolithic chip, the embedded control applications. The AT89S52 provides the following standard features: 8K bytes of Flash, 256 bytes of RAM, 32 I/O lines, Watchdog

timer, two data pointers, three 16-bit timer/counters, a six-vector two-level interrupt architecture, a full duplex serial port, on-chip oscillator, and clock circuitry. In addition, the AT89S52 is designed with static logic for operation down to zero frequency and supports two software selectable power saving modes. The Idle Mode stops the CPU while allowing the RAM, timer/counters, serial port, and interrupt system to continue functioning. The Power-down mode saves the RAM con-tents but freezes the oscillator, disabling all other chip functions until the next interrupt. The connections are shown in fig 4.

Fig 4. AT89S52 connection

B. MAX232

The MAX232 is an integrated circuit that converts signals from an RS-232 serial port to signals suitable for use in TTL compatible digital logic circuits. The MAX232 is a dual driver/receiver and typically converts the RX, TX, CTS and RTS signals. The drivers provide RS-232 voltage level outputs (approx. ± 7.5 V) from a single + 5 V supply via on-chip charge pumps and external capacitors. This makes it useful for implementing RS-232 in devices that otherwise do not need any voltages outside the 0 V to + 5 V range, as power supply design does not need to be made more complicated just for driving the RS-232 in this case. The receivers reduce RS-232 inputs (which may be as high as ± 25 V), to standard 5 V TTL levels. These receivers have a typical threshold of 1.3 V, and a typical hysteresis of 0.5 V. The later MAX232A is backwards compatible with the original MAX232 but may operate at higher baud rates and can use smaller external capacitors – 0.1 μF in place of the 1.0 μF capacitors used with the original device. The corresponding figure is shown in fig 5.


COS0202-3

Fig 5. MAX232 connection

A. RS232

In RS-232, user data is sent as a time-series of bits. Both synchronous and asynchronous transmissions are supported by the standard. In addition to the data circuits, the standard defines a number of control circuits used to manage the connection between the DTE and DCE. Each data or control circuit only operates in one direction that is, signaling from a DTE to the attached DCE or the reverse. Since transmit data and receive data are separate circuits, the interface can operate in a full duplex manner, supporting concurrent data flow in both directions. The standard does not define character framing within the data stream, or character encoding. The corresponding figure is shown in figure 6.

Fig 6. RS232 connection

C. ULN2003

The ULN2003 is a monolithic high voltage and high current Darlington transistor arrays. It consists of seven NPN Darlington pairs that feature high-voltage outputs with common-cathode clamp diode for switching inductive loads. The collector-current rating of a single Darlington pair is 500mA. The Darlington pairs may be paralleled for higher current capability. Applications include relay drivers, hammer drivers, lamp drivers,

display drivers (LED gas discharge), line drivers, and logic buffers. The ULN2003 has a 2.7k series base resistor for each Darlington pair for operation directly with TTL or 5V CMOS devices. The diagram of ULN 2003 is shown in figure 7.

Fig 7 . ULN2003 connection

B. PROXIMITY SENSOR

A proximity sensor is a sensor able to detect the presence of nearby objects without any physical contact. A proximity sensor often emits an electromagnetic or electrostatic field, or a beam of electromagnetic radiation (infrared, for instance), and looks for changes in the field or return signal. The object being sensed is often referred to as the proximity sensor's target. Different proximity sensor targets demand different sensors. For example, a capacitive or photoelectric sensor might be suitable for a plastic target; an inductive proximity sensor requires a metal target. The maximum distance that this sensor can detect is defined "nominal range". Some sensors have adjustments of the nominal range or means to report a graduated detection distance. Proximity sensors can have a high reliability and long functional life because of the absence of mechanical parts and lack of physical contact between sensor and the sensed object. Proximity sensors are also used in machine vibration monitoring to measure the variation in distance between a shaft and its support bearing. This is common in large steam turbines, compressors, and motors that use sleeve-type bearings.IEC 60947-5-2 define the technical details of proximity sensors. A proximity sensor adjusted to a very short range is often used as a touch switch. A


COS0202-4

proximity sensor is divided in two halves and if the two halves move away from each other, then a signal is activated. A proximity sensor can be used in windows, and when the window opens an alarm is activated. The figure is shown in fig 8.

fig 8. The design of infrared proximity sensor courtesy: Autonics sensor and controller [7]

IV DEVELOPMENT AND IMPLEMENTATION OF FUZZY LOGIC IN THE SYSTEM

FUZZY LOGIC:

Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s. Dr. Zadeh was working on the problem of computer understanding of natural language. Number of partial truths which we aggregate further into higher truths which in turn, when certain thresholds are exceeded, cause certain further results such as motor reaction. A similar kind of process is used in artificial computer neural network and expert systems. It may help to see fuzzy logic as the way reasoning really works and binary or Boolean logic is simply a special case of it.

The implementation of fuzzy logic is shown in figure 9,10 11 ,12 and 13. The input is taken as Level , abbreviated as L1 ,L2 and L3. The Corresponding output is taken as Valve Opening.

Fig 9. Bottle filling system with level as input and valve opening as output

Fig 10. Membership function plot of input variable “Level”

Fig 11. Membership Function plot of Output variable “valve opening”


COS0202-5

Fig 12. Rules of bottle filling system

Fig 13. Rule viewer of bottle filling system

V. POSSIBLE PROBLEMS ON BOTTLING PLANT

During transportation of Bottles several problem can occur , depending on shape and size of bottle , environmental conditions (temperature and humidity can affect the sliding of bottle).For instance one bottle gets stuck on the guide then it blocks all other bottles which are behind it. The only solution in this situation is manual intervention of plant supervisor. But during the standard working of plant often the supervisor has to change the pressure level of the pipe to maintain a constant bottle flow. But sometimes operators do not know what to do in particular situation. This is due to the complexity of plant .It is clear that this is the typical fuzzy situation, so we tried to design a fuzzy controller to modify the

pressure levels in order to maintain a constant speed and flow of Bottle in entire plant and avoiding Jamming Condition.

VI. CONCLUSION

The study shows how Fuzzy Logic is used for designing the automatic bottle filling system. Bottled water is the fastest growing beverage industry in the world. A proximity sensor is employed for detecting the presence of bottle and Light dependent resistor and comparator is employed for sensing the level of liquid inside the bottle. Fuzzy Logic is employed for regulating the liquid level. There is no new investment on machine , conveyors, or anything else.

REFERENCES

[1] WATER PET BOTTLE, From trash to treasure, august 2001 .available at http://www.designboom.com

[2] OMRON Tech Focused for automation solution.

[3] E.Mainardi, M.Bonfe, M.Golfieri, “Fuzzy controller for an air conveyor bottling plant”, 16

th

IEEE conference on control applications, Singapore 1-3 0ctober, 2007

[4]Chwen –Tzeng Su,” A Fuzzy Approach For Part Family Formation “, International IEEE, page 289 (1995).

[5] Santos, CM.P; Ferreira, M.J,” Control Of an Industrial desktop Robot Using computer Vision and fuzzy rules”, IEEE International , Volume 3 ,page 1297-1303, 2005.

[6] Biglarbegian, M; Melek, W; Mendel, M;” Design of Novel Interval type- 2 Fuzzy Controller For Modular and reconfigurable robots: Theory and Experiments” IEEE, Volume pp,Issue:99, page (1),2010.

[7] Autonics sensor and controller. Available at http://www.autonics.com.


COS0203-1

Real Time Control of Rotary Flexible Joint with LQR and Fuzzy

Controller




Abstract

In the position control of robot arm, Rotary

Flexible Joint (ROTFLEX) constitutes main

function of positioning the arm. The precision

with which the arm is controlled depends on the

response of motor and on different parameters

of flexible joint. This paper concentrates on

development of Linear Quadratic Regulator

(LQR) and fuzzy controller for flexible joint

operated by a dc servomotor. The comparison of

performance of real time system with simulation

is done by simultaneously observing measured

and simulated angular position of flexible joint.

It is found that the response is improved with

fuzzy controller as compared to LQR.

1. Rotary Flexible Joint (Rotflex)

The rotary flexible joint consists of a rotary

sensor mounted in a solid aluminum frame and

is designed to mount to a Quanser rotary servo

plant. The sensor shaft is aligned with the motor

shaft. One end of a rigid link is mounted to the

sensor shaft. The link rotation is counteracted by

two extension springs anchored to the solid

frame resulting in an instrumented flexible joint.

The spring anchor points are adjustable to three

locations to obtain various stiffness constants.

Three types of springs are supplied with the

system resulting in a total of 9 possible stiffness

values. The link is also adjustable in length thus

allowing for variations in inertia. This system is

similar in nature to the control problems

encountered in large geared robot joints where

flexibility is exhibited in the gearbox.

A rigid beam is mounted on a flexible joint

that rotates via a DC motor. The joint deflection

is measured using a sensor. The rotary flexible

joint is an ideal experiment intended to model a

flexible joint on a robot or spacecraft. This

experiment is also useful in the study of

vibration analysis and resonance.

2. Features of ROTFLEX.

High Quality Aluminum chassis with

precision crafted parts

· High Resolution Encoders to sense arm

position

· Variable Loads and Spring Anchors

· Variable Spring Stiffness

· Fully documented system models &

parameters

· Open Architecture Facilitates Matlab /

Simulink

Design

· Modular Design

· 112 Distinct Configurations!

3. Design & Simulation

The rotary flexible joint is an ideal

experiment intended to model a flexible joint on

a robot or spacecraft. This experiment is also

useful in the study of vibration analysis and

resonance. The purpose is to design a state-

feedback controller that will place the tip of the

arm (θ + α) at a given command. Here

requirement is to design a full state feedback

controller using the LQR method to calculate

the gains.



COS0203-2

The first part of this will be to design a state-

feedback controller that will meet the required

specifications. The method of calculating the

feedback gains will be the LQR function in

MATLAB's control systems toolbox. The

MATLAB LQR function returns a set of

calculated gains based on the system matrices A

& B and the design matrices Q & R. In this

section begin the iterative design process by

varying Q & R and taking notice on the effect

those changes on the simulated system response

and the closed-loop poles (eigen values).Fix the

Q matrix to be only diagonal. This will allow

you to vary 4 parameters for Q (q1,q2,q3,q4)

and one parameter for R (r1) (R in this case is

scalar as there is only 1 input and therefore 1

control signal).

Q = diag ([10 100 1 1]) & R = 1

This model is a simulation of the Flexible

Joint system with a feedback law u = -kx. The

gain vector k is set by the LQR function. The

largest element of Q should be the one that is

associated with α.Notice that the largest value in

Q is q2. The default Q & R values are

Q = diag ([10 100 1 1]) & R = 1.

4. Basic controllers

An automatic controller compares the actual

values of plant output with the reference input

through feedback elements and produces an

actuating signal, that actuates the control

elements thus reducing the deviation to zero as

to a small valued. The basic controllers

depending upon the manner in which the control

signal is produced.

5. Linear Quadratic Regulator (LQR)

A most effective and widely used technique of

linear control systems design is the optimal

linear quadratic regulator (LQR). The simplified

version of the LQR problem is to find the

control such that the performance index is

minimized for the system.

The formulation of the problem follows.

Given the linear system .

x Ax Bu

y Cx

Find a control function u(t) that will minimize

the cost function J given by

0

1( ' ' )

2J x Qx u Ru dt

The function inside the integral is a quadratic

form and the matrices Q and R are usually

symmetric. It is assumed that R is positive

definite. (i.e. it is symmetric and has positive

eigen values) and Q is positive semi

definite(i.e. it is symmetric and its eigenvalues

are nonnegative).These assumptions imply that

the cost is nonnegative, so its minimum value is

zero. For the cost function to achieve its

minimum value both x and u must go to zero.

This type of control problem is called a

regulator problem. When the state vector is to

track nonzero values, J can be redefined to

create an optimal servo mechanism (tracking)

problem. Regulator behavior is important for

control systems of many types (e.g., attitude

control of satellites or spacecraft, where a zero

reference should be maintained in spite of

disturbances).

6. Properties of the LQR design

LQR has many desirable properties. Among

them are good stability margins and sensitivity

properties. Most of these properties can be

derived using the return-difference inequality

derived by Kalman.

6.1Industrial Applications

Multivariable design based on LQR theory

and the Kalman filter accounts for thousands of

real-world applications.

Geostationary satellite tracking


COS0203-3

Zinc Coating-Mass estimation in

continuous galvanizing lines

Roll-Eccentricity estimation in thickness

control for rolling mills

Vibration control in flexible structures

7. Fuzzy Logic Controller

The tasks of a controller are more complex

than explained earlier for majority of the

systems. In such situations, a fuzzy controller

can be developed to control the system. Fuzzy

logic control is proved to be effective for

complex, non-linear and imprecisely defined

process for which standard model based control

techniques are impractical.

In most of the systems the changes in

parameters due to nose and major disturbances

do occur. In such cases the performance of PID

controller is not that good. Better control

techniques are needed to improve the system

performance.

As fuzzy logic controller is strongly based on

the concepts of fuzzy sets and relations and

linguistic variables, the advantages of fuzzy

logic includes quicker development cycles and

superior performance. Basically, conventional

control systems have five development steps:

developing a mathematical model of system,

developing a mathematical model for a

controller, analyzing the equations, converting

the model to a circuit and building the circuit.

Fuzzy logic, on the other hand has just three:

developing a fuzzy model, simulation and

compilation.

8. Simulations & Results

8.1 Simulation Of ROTFLEX With LQR

Procedure for doing the experiment

Before starting the experiment check the

following:

a. The arm deflection signal (α) should be

connected to encoder channel #1 and the

servomotor's position signal (θ) should be

connected to encoder channel #0.

b. Analog Output channel #0 should be

connected to the UPM (Amplifier) and from the

amplifier to the input of the servomotor. This

system has one input (Vm) and two outputs (θ

& α).

1. Click on start then click on wincon server.

2. Click on start then click on wincon client.

3. Click on start then click on MATLAB and

run the setup

file. This MATLAB script file will setup all the

specific

System parameters and will set the system

state-space

Matrices A, B, C & D.The MATLAB LQR

function

returns a set of calculated gains based on the

system

matrices A & B and the design matrices Q &

R.

4. Now run the file by click on the “run” in the

command window or pressing F5.Then

calculated gains are displayed in the

MATLAB window.

5. Open simulink in matlab window, then run

the simulink file. This model is a simulation of

the Flexible Joint system with a feedback law u

= -kx.The gain vector k is set by the LQR

function. This model has the I/O connection

blocks linking to the physical plant as well as a

simulated block to compare real and simulated

results

6. Then in the simulink model (fig 1.1) set the

amplitude as 20 by double clicking on it.

7. Fig 1.1 above depicts the Rotary Flexible

Joint controller developed for this experiment.

Notice that both the actual system and an exact

simulation are running in parallel thus allowing

comparing the actual and simulated results.

Before “Building” this controller, make sure

that the state-feedback gain k is set according to

final design parameters. Now proceed to

“Build” the controller through the WinCon

menu. After the code has compiled, start the

controller through WinCon and open up two


COS0203-4

Scopes, for theta (measured and simulated

together). Then measure the rise time, steady

state error of measured and simulated theta

Fig 1. 1Circuit diagram of Flexible Joint Controller with LQR

Fig1.2 Simulated response of simulated theta with LQR

Fig 1.3 Simulated response of measured theta with LQR

9. Simulation Of ROTFLEX With Fuzzy Logic Controller.


COS0203-5

Fig 1.1 is modified as fig 1.4 to control with fuzzy logic controller.

Fig 1.4 Circuit diagram of Flexible Joint Controller using fuzzy logic controller


COS0203-6

9.1 Procedure for doing the experiment

Before starting the experiment check the

following:

a. The arm deflection signal (α) should be

connected to encoder channel #1 and the

servomotor's position signal (θ) should be

connected to encoder channel #0.

b. Analog Output channel #0 should be connected

to the UPM (Amplifier) and from the amplifier to

the input of the servomotor. This system has one

input (Vm) and two outputs (θ & α).

1. Click on start then click on wincon server.

2. Click on start then click on wincon client.

3. Click on start then click on MATLAB then in

MATLAB window run the setup file. This

MATLAB script file will setup all the specific

system parameters and will set the system state-

space matrices A, B, C & D. The MATLAB LQR

function returns a set of calculated gains based on

the system matrices A & B and the design

matrices Q & R.

4. Further in the script file, there is a section that

will set User-defined controller specification

section set Student_Config = 'MANUAL’. Save

the file, run it. Check whether the file runs or not

by checking whether design

Specifications file appear in the workspace

directory of mat lab window .if appears, Then

minimize it.

5. Click on start in the mat lab window on the

bottom left corner of computer screen, then

toolboxes, then in fuzzy logic click on the FIS

editor.In FIS editor window click on New then on

Mandani model.

6. In FIS editor double click on “input”, then a

membership editor will appears as below .In this

click on edit and mention how many membership

functions are present. Here there are 11(later 15)

membership functions

and for each membership functions define a name.

Then close it.

7.In FIS editor double click on “output”, then a

membership editor will appears as below In this

click on edit and mention how many membership

functions are present. Here there are 11 (later 15)

membership functions and for each membership

functions define a name. Then close it.

NOTE: here first 11 rules then 15 rules are used

to observe how FLC varies for different rules and

to improve the simulation results.

In FIS editor double click on “mandani”, then a

rule editor will appears as below In this rule editor

edit the rules as required. Here first there are 11

rules (then 15 rules for better results), Then save

and close the FIS editor by giving a file name.

Then go to file in the FIS editor import the .FIS

file from disk and export the .FIS file to

workspace. In mat lab window, in workspace

directory check whether .FIS named file appears

or not. If the file appears then experimental

procedure is in correct way.

Then run simulink model .This model has the I/O

connection blocks linking to the physical plant as

well as a simulated block to compare real and

simulated results

Then in the simulink model (fig 1.4) set the

amplitude as 20 by double clicking on it. For gains

before and after fuzzy logic controller with

viewer, set gain=0.04 and gain=13.Double click

on fuzzy logic controller with rule viewer in fig

1.4 .Here type the FIS editor file name, and then

click on ok. Now proceed to “Build” the controller through

the WinCon menu. After the code has compiled, start the

controller through WinCon and open up two Scopes, for

theta (measured and simulated together).Then measure the

rise time, steady state error of measured theta and the values

are tabulated.


COS0203-7

Fig 1.5 Simulated response of simulated theta

Fig 1.6 Simulated response of measured theta (a) 11 rules in rule editor

Fig 1.7 Simulated response of measured theta

(b) 15 rules in rule editor

10 Conclusion

The following table is the comparison between

LQR and Fuzzy logic controller.

Controller Rise time

Steady state error

Linear Quadratic

Regulator (LQR) 770 ms - 11 %

Fuzzy logic

controller 240 ms + 13 %

The comparison of performance of real time

system with simulation is done by simultaneously

observing measured and simulated angular

position of flexible joint. It is found that the

response is improved with fuzzy controller as

compared to LQR.

11 References

[1]. Fuzzy control -Kevin M. passion Stephen

yurkovich.

[2]. Fuzzy Logic (intelligence control &

information) -John Yen & Reza Langari.

[3]. Automatic control systems - Kuo


COS0203-8

[4]. Control system Engineering – I.J.Nagrath -

M.Gopal

[5]. Modern control Engineering – Katsuhiko

ogata

6[]. Control system design -Graham C.Goodwin,

Stefan F.Graebe -Mario

E.Salgado-Pearson education

[7]. Design of feedback control system 4th edition

Oxford Indian edition Stefani-Shahian -

Savant –Hostetter

[8]. SRV02-(E; EHR) (T)-Rotary Servo Plant

(Quanser) - User Manual

[9]. SRV02-Series-ROTFLEX – Rotary Flexible

Joint (Quanser)- User Manual

[10]. SRV02-Series-Rotary Experiment # 4-

Flexible Joint (Quanser)-Student Handout.


COS0204-1

Fuzzy Logic Control for a Speed Control of

Induction Motor using Pulse Width Modulation




Abstract

This paper presents design and implements a voltage

source inverter type space vector pulse width modulation

(PWM) for control a speed of induction motor. This

scheme leads to be able to adjust the speed of the motor

by control the frequency and amplitude of the stator

voltage, the ratio of stator voltage to frequency should be

kept constant. The fuzzy logic controller is also

introduced to the system for keeping the motor speed to be

constant

When the load varies. The experimental results in testing

the 0.22 kW induction motor from no-load condition to

rated condition show the effectiveness of the proposed

control scheme. Keywords—Fuzzy logic control, space vector pulse

width modulation, induction motor.

1. Introduction

The Pulse Width Modulation (PWM) method is an

advanced, computation-intensive PWM

method and possibly the best among all the PWM

techniques for variable frequency drive application.

Because of its superior performance characteristics, it has

been finding widespread application in recent year. The

PWM methods discussed so far have only considered

implementation on half bridges operated independently,

giving satisfactory PWM performance. With a machine

load, the load neutral is normally isolated, which causes

interaction among the phases.

This interaction was not considered before in the PWM

discussion [1]-[4]. Recently, Fuzzy logic control has

found many applications in the past decade. This is so

largely because fuzzy logic control has the capability to

control nonlinear, uncertain systems even in the case

where no mathematical model is available for the

controlled system. However, there is no systematic

method for designing and tuning the fuzzy logic

controller.

This means that if the a reliable expert knowledge is not

available or if the controlled system is too complex to

derive the required decision rules, development of a fuzzy

logic controller become time consuming and tedious or

sometimes impossible. In the case that the expert

knowledge is available, fine-tuning of the controller might

be time consuming as well. Furthermore, an optimal fuzzy

logic controller can not be achieved by trial-and-error.

These drawbacks have limited the application of fuzzy

logic control. Some efforts have been made to solve these

problems and simplify the task of tuning parameters and

developing rules for the controller. These approaches

mainly use adaptation or learning techniques drawn from

artificial intelligence or neural network theories.

Application of fuzzy logic control for the control a speed

induction motor using space vector pulse width

modulation is quite new [5].

This paper presents design and implements a voltage

source inverter type space vector pulse width modulation

for control a speed of induction motor. The paper also

introduces a fuzzy logic controller to the SVPWM in

order to keep the speed of the motor to be constant when

the load varies. The speed motor control system is set up

for testing.

The aim of this paper is two-fold. The first is shown the

dynamics response of speed with design the fuzzy logic

controller to control a speed of motor for keeping the

motor speed to be constant when the load varies. The

second aim is shown the phase voltage and line current

waveforms.

2. Inverter for ac drives

2.1 Space Vector Pulse Width Modulation

The SVPWM method considers this interaction of the

phase and optimizes the harmonic content of the three

phase isolated neutral load as shown in Fig. 1.

Fig. 1 Voltage source inverter type 3 phase


COS0204-2

The three phase sinusoidal and balance voltages

given by the equations as follows:

Are applied to the three phase induction motor,

using Eq. (4). A three phase bridge inverter, From Fig. 1,

has 8 permissible switching states. Table I gives summary

of the switching states and the corresponding phase-to-

neutral

voltage of isolated neutral machine.

2.2 Simulink Implementation

To implement the algorithm in Simulink, we shall first

assume that the three-phase voltages at the stator

terminals must have the following from Eqs. (1)-(3), the

frequency f and the amplitude m V are variables.

However, theV f control algorithm implies that there is a

relationship between the amplitude of the voltage and the

frequency, i.e. the ration between the two quantities is

constant [2],[3].

3. Design Of A Fuzzy Logic Controller

In drive operation, the speed can be controlled indirectly

by controlling the torque which, for the normal operating

region, is directly proportional to the voltage to

frequency.

The speed is controlled by fuzzy logic controller whose

output is the reference current of the inner dc current

controller. The torque is controlled by varying the dc

current. The drive performance of SVPWM is improved

by employing 2 sets of fuzzy logic controllers. From Fig.

4, one set of fuzzy logic controller is used in the inner

loop for controlling the torque of the motor which is

proportional to DC link current Idc, and another set is


COS0204-3

used in the outer loop for controlling the actual motor

speed .

Therefore, the fuzzy logic controllers in the paper will

result the higher accuracy in controlling the v/f/F. A fuzzy

logic controller is proposed to control the speed of the

motor to be constant when the load varies. The speed

error e(k) and the change of speed error ce(k) are

processed through the fuzzy logic controller whose output

is the voltage command * dc I . The current error is

usually processed by current regulator to produce a

control frequency *e ù . This control frequency adjusts the

v/f of SVPWM such that the desired speed of the motor

can be obtained. In the design of a fuzzy logic controller,

seven membership functions were used for both error and

change of error. Membership functions were constructed

to represent the input and output value. The fuzzy logic

controller consists of three stages: fuzzification, control

rules evaluation and defuzzification. Consider the fuzzy

speed control system , where the input signal are e and ce

and the output signal is du, as explained before. Fig. 5

shows the fuzzy sets and corresponding triangular MF

description of each signal. The fuzzy sets are as follows :

Z = Zero, PB = Positive Big, NB = Negative Big, PS =

Positive Small, NS = Negative Small, NVS = Negative

Very Small, PM = Positive Medium NM = Negative

Medium, PVS = Positive Very Small [6].

The universe of discourse of all the variables , covering

the whole region , is expressed in per unit values. All the

MFs are asymmetrical because near the origin, the signals

require more precision. There are seven MFs for e(pu)

and ce(pu) signal , whereas there are nine MFs for the

output. All the MFs are symmetrical for positive and

negative values of the variables. Fig. 6 shows the

corresponding rule table for the speed controller. The top

row and left column of the matrix indicate the fuzzy sets

of the variables e and cue, respectively , and the MFs of

the output variable du(pu) are shown in the body of the

matrix. There may be 7*7 = 49 possible rules in the

matrix , where a typical rule reads as: IF e(pu) is PS AND

ce(pu) is NM THEN du(pu) is NS. Some blocks in the

rule table may remain vacant giving less number of rules.

4. Experimental Setup

The experimental set-up, illustrated in Fig. 7,

implemented to a three phase induction motor which has

the detail as follows: 0.22 kW, 230/400V, 1.03/0.59 A,

50Hz, P.F 0.8 lag and 1410 rpm. The speed of motor

ranging from 0 to 1500 rpm is measured by incremental

encoder 3600 pulse/rev. All current and voltage are

measured using LEM sensors, and both of them are then

transformed to be a voltage ranging from 0 to 10 volts

which will be the input of A/D respectively. This scheme

enables the user to adjust the speed of the motor by the

duty cycle of the V/F operating in SVPWM mode. The

performances of a linear control technique implemented

on a fuzzy logic controller to control speed of motor using

dSPACE the real-time DS1104’TMS3204 DSP Controller


COS0204-4

Board along with the Matlab/Simulink tool with sampling

time 1 ms as shown in Fig. 7 [7]-[11].

4.1 Step Response of Speed

To evaluate the performance of the system, a series of

measurements has been accomplished. The measurements

can be divided in two groups: the first is a step change of

the speed reference at constant load torque and the second

is a step change of the load torque at constant speed

reference. Figs. 8- 9 as shown performance of the fuzzy

logic controller with a fuzzy tuning rule based on step

response of speed control. To be tested time response of

speed, duty cycle and line current via the step change of

speed reference 300 to 1200 rpm with the load torque

equal to zero and equal to rated respectively. Figs. 10-11

as shown time response of speed, duty cycle and line

current via the step change of the load torque at constant

speed reference 600 and 1200 rpm respectively.

From the results tested the performance of

controller by a step change of the speed reference at

constant load torque as shown in Figs. 8-9, it’s found that

the Rise time 2 r p

Maximum overshoot 12 p

load torque at constant speed reference as shown in Figs.

10-11, it’s found that the Settling time 15 s

experimental results obtained, the proposed fuzzy logic

controller can keep the motor speed to be constant at the

speed ranging from 300 to 1200 rpm. Figs. 12-14 as

shown steady state error of speed at reference speed 300,

600 and 1200 rpm rated load respectively. It’s found that

it have state error ±10 rpm.


COS0204-5

4.2. The Phase Voltage and Input Line Current

Waveforms

The line voltage and line current are measured;

they are measured by using LEM sensors with ratio

Amp/volt and ratio 100 V/volt. All data of signal are kept

on of digital storage oscilloscope. The waveforms of

SVPWM, phase voltage an V and line current a I are

measured digital storage oscilloscope as shown in Figs.

15-20.


COS0204-6

5. Conclusion

The Fuzzy logic controller is applied to speed signal

model of motor and is then downloaded to dSPACE

through Simulink. The experimental results are analyzed

in testing the 0.22 kW induction motor from zero load

condition to rated condition, it’s found that the speed of

the induction motor can be controlled. In addition, the

motor speed to be constant when the load varies.

References

[1] Andrzej M. Trzynadlowski, Introduction to Modern Power

Electronics, Copyright© 1998 by John Wiley & Sons, Inc. All

rights reserced.

[2] Bimal K.Bose, Modern Power Electronics and AC Drives, ©

2002 Prentice Hall PTR.

[3] W. Leonharn, Control of Electrical Drives, Springer-Verlag

Berlin Heidelberg, New York, Tokyo, 1985.

[4] F. Ashrafzadeht, E.P. Nowickit, and J.C. Samont, “A Self-

Organizing and Self-Tuning Fuzzy Logic Controller for Field

Oriented Control of Induction Motor Drives”, 0-7803-3008-0/95

$4.00© 1995 IEEE, pp. 1656-1662.

[5] Zdenko Kovaccic and Stjepan Bogdan, Fuzzy Controller

design Theory

and Applications, © 2006 by Taylor & Francis Group.

International, 2002.

[6] Hoang Le-Huy and Maher Hamdi, “Control of Direct-Drive

DC Motor by Fuzzy Logic”, 0-7803-1462-x/93$03.00 © 1993

IEEE, pp. 732-738.


COS0205-1

Optimal Placement Techniques of FACTS Controllers

in Multi-Machine Power System Environments: A

Literature Survey

Bindeshwar Singh, N. K. Sharma and A. N. Tiwari

Abstract- This paper presents exhaustive review of various

methods/techniques for optimal choice and allocation of

FACTS controllers. Authors strongly believe that this

survey article will be very much useful to the researchers

for finding out the relevant references in the field of

placement of FACTS Controllers.

Index Terms- Flexible AC Transmission System (FACTS),

FACTS Controllers, Placements, Voltage Stability.

I.INTRODUCTION

HE drive towards deregulated environment may result in

simultaneous installation of different FACTS controllers

in power system. These multiple FACTS controllers have the

potential to interact with each other. This interaction may

either deteriorate or enhance system stability depending upon

the chosen controls and placement of FACTS controllers.

Hence there is a need to study the interaction between the

FACTS controllers.

The various interactions can potentially occur between the

different FACTS controllers, as well as, between FACTS

controllers and Power System Stabilizers (PSS) in a multi-

machine power system environment. These likely interactions

have been classified into different frequency ranges and

various interaction problems between FACTS controllers or

FACTS to PSS’s from voltage stability/ small signal stability

viewpoint are presented in [1]-[2].

There are several methods proposed in literatures [3]-[86],

[87]-[111], for placement of FACTS controllers in multi-

machine power systems from different operating conditions

viewpoint. References [3]-[5], classify three broad categories

such as a sensitivity based methods, optimization based

method, and artificial intelligence based techniques for

placement of FACTS controllers from different operating

conditions viewpoint in multi-machine power systems. The

various sensitivity based methods have been proposed in

literatures includes eigen-value analysis based methods [6]-

[12], modal analysis techniques [13]-[15], index methods [16]-

[23], residue-based methods [24]-[25],[43],[98]-[99], and

other sensitivity based methods [26]-[37],[53],[87]-[97],[111].

The various optimization based methods have been proposed

in literatures that includes non-linear optimization

programming techniques [38],[39],[103], mixed integer-

optimization programming techniques [40]-[42],[100]-[101],

dynamic optimization programming algorithms [44], hybrid

optimization programming algorithms [45], bellmann’s

optimization principle [46], decomposition coordination

methods [47]-[48], curved space optimization techniques

[104]. The various artificial intelligence (AI) based methods

proposed in literature includes genetic algorithms (GA) [49]-

[64],[105]-[106], [110], tabu search algorithms [65],[66],

simulated annealing (SA) based approach [69]-[70],[107],

particle swarm optimization (PSO) techniques [71]-[73],[80],

artificial neural network (ANN) based algorithms [74]-[76],

ant colony optimization (ACO) algorithms [77]-[78], graph

search algorithms [79], fuzzy logic based approach [81]-[82],

other techniques such as norm forms of diffeomorphism

techniques [83], evolution strategies algorithms [84],[86],

improved evolutionary programming [68], gravitational

optimization techniques [85], benders decomposition

techniques [42], augmented Lagrange multiplier approach

[67], hybrid meta-heuristic approach [102], heuristic and

algorithmic approach [108], energy approach [109].

This paper is organized as follows: Section 2 presents the

review of various techniques/methods for placements of

FACTS controllers in multi-machine power systems. Section 3

presents the summary of the paper. Section 4 presents the

conclusions of the paper.

II. CLASSIFICATIONOF FACTS CONTROLLERS

ALLOCATION TECHNIQUES

Three broad categories of allocation techniques for

determining best suited location of FACTS controllers are

sensitivity based methods, optimization based method, and

artificial intelligence based techniques [3]-[5].

A. Sensitivity Based Methods

There are various sensitivity based methods such as a modal

or eigen-value and index analysis. An eigen-value analysis

based techniques has been proposed in [6] for the selection of

parameters of stabilizers in multi-machine power system to

enhance the damping of the power system oscillations.

Reference [7], presents an eigen-value analysis based

algorithms such as a participation factor method for

identification of optimum location of power system stabilizers

to enhance the damping of power system oscillations. Many

literatures shows the existing methods of identifying the

optimum sites for installing PSSs in multi-machine power

systems are restricted to the sequential PSS application, which

T


COS0205-2

considers the enhancement of damping of just one critical

electro-mechanical modes at a time and the eigen structure

analysis of the open loop system, which does not take the

control matrix (the B matrix in the linearized model dX/dt=

AX+BU for power system) into considerations. An eigen-

value analysis based techniques has been proposed for

identifying the optimum sites for installing power system

stabilizers (PSSs) in multi-machine power systems in [8]. The

advantages of this proposed method is that it can identify the

optimum sites for installing PSS so that several electro-

mechanical modes are damped out simultaneously and it takes

both the eigen structure of the open loop system and the

control matrix into consideration. Reference [9], suggests an

eigen-value analysis based approach for identifying the most

effective FACTS controllers, locations, types and ratings that

increase asset utilization of power systems. Reference [10],

uses participation factor method has been suggested for the

critical mode are used to determine the most suitable sites for

SVC (Static Var Compensator) for system reinforcement. In

[11]-[12], an eigen-value analysis based approach has been

proposed for find the optimal location and rating of FACTS

controllers (Static Var Compensator (SVC) and Thyristor

Controlled Series Controller(TCSC)) and a continuation

power flow is used to evaluate the effects of SVC and TCSC

devices on power loadability). In [13] , a modal analysis

algorithm has been suggested for allocation and control of

FACTS devices for steady-state stability enhancement of large

scale power system. A modal analysis algorithm has been

suggested for placement of SVCs and selection of stability

signals in power systems environments [14]. A new eigen

solution free method of modal control analysis for the

selection of the robust installing locations and feedback

signals of FACTS based stabilizers in large–scale power

systems is presented in [15]. An index based approach known

as a Location Index for Effective Damping (LIED) method has

been proposed for identifying the location of SVC and a

Variable Series Capacitor (VSrC) in large-scale power

systems for damping effectively in [16]. A structure

preserving energy margin sensitivity based analysis has been

addressed for determine the effectiveness of FACTS devices

to improve transient stability of a power system in [17]. A

controllability index method has been proposed for select the

input signals for both single and multiple FACTS devices in

small and large power systems for damping inter-area

oscillations in [18]. Different input output controllability

analysis are used to asses the most appropriate input signals

(stability signals) for the SVC, the Static Synchronous Series

Compensator (SSSC) and the Unified Power Flow Controller

(UPFC) for achieving good damping of inter-area oscillations.

A new method called the Extended Voltage Phasors Approach

(EVPA) has been suggested for placement of FACTS

controllers in power systems for identifying the most critical

segments/bus in power system from the voltage stability view

point in [19]. In [20]-[23], an index based method has been

addressed for determine the suitable locations of FACTS

devices in power system environments. A residues based

approach has been proposed for allocation of FACTS

controllers in power system to enhance the system stability

[24]-[25]. An efficient algorithm for the solution of two

important problems in the area of damping control of electro-

mechanical oscillations in large scale power systems has been

proposed in [26]. The proposed algorithms allow the

determination the most suitable generators for installing power

system stabilizers and the most suitable buses in the system

for placing SVC in order to damp the critical modes of

oscillation. A sensitivity based approach has been proposed

for placement of FACTS controllers in open power markets to

reduce the flows in heavily loaded lines, resulting in an

increased loadability, low system loss, improved stability of

the network, reduced cost of production and fulfilled

contractual requirement by controlling the power flows in the

network in [27]-[28] . A sensitivity based approach called Bus

Static Participation Factor (BSPF) has been proposed for

determine the optimal location of static VAR compensator

(SVC) for voltage security enhancement in [29]. A sensitivity

based approach has been proposed to determine the placement

of TCSC and UPFC for enhancing the power system

loadability [30]. In [31], a sensitivity analysis method has been

proposed for determine the optimal placement of static VAR

compensator (SVC) for voltage security enhancement in

Algerian Distribution System. In [32], a sensitivity analysis

and evolutionary programming techniques has been proposed

for determine the optimal placement of UPFC in power system

from the operational planning viewpoint. Sensitivity analysis

is superior when compared to the others as sensitivity analysis

gives the best possible installation location. References [33]-

[37], presents a sensitivity based approach has been proposed

for optimal placement of UPFC to enhance voltage stability

margin under contingencies. Reference [87], suggests a

normal form analysis approach based on sensitivity indices is

used for sitting power systems stabilizers (PSS) in power

systems network. Reference [88], suggested a Trajectory

Sensitivity Analysis (TSA) technique for the evaluation of the

effect of TCSC placement on transient stability. A sensitivity

based technique is used for placement of Static Synchronous

Series Compensator (SSSC) in power system network in [89].

Sensitivity based screening technique for greatly reducing the

computation involved in determining the optimal location of a

Unified Power Flow Controller (UPFC) in a power system

[90]. A sensitivity analysis and evolution programming

technique has been proposed for placement of UPFC in a

power system in [91]. In [92], a sensitivity based technique

used for determine the minimum amount of shunt reactive

power (VAr) support which indirectly maximizes the real

power transfer before voltage collapse is encountered.

Sensitivity information that identifies weak buses is also

available for locating effective VAr injection sites. A

sensitivity based technique is used for determine optimal

placement of Static Synchronous Compensator (STATCOM)

and Unified Power Flow Controllers (UPFC) to enhancement

of Dynamic Available Transfer Capability (ATC) under

different contingencies in New England System [93]. A

sensitivity factor based approach has been used in [94] for the

optimal placement of the TCSC to minimize the congestion


COS0205-3

cost. In [95], a second-order sensitivity analysis technique

used for optimal location of SVC and TCSC in power system.

An eigen-value sensitivity based approach has been used for

location and controller design of TCSC to enhance damping

power system oscillations [96]. In [97], a new sensitivity

factor, called System Loading Distribution Factor is used for

determine the optimal location of UPFC in power system. A

residues factor method has been used for determination of

optimal location of the TCSC device to damp out the inter-

area mode of oscillations [98]. In [99], a residues method

based on sensitivity analysis technique is used for determine

optimal location of the SVC to enhance the damping power

system oscillations. A new approach based on sensitivity

indices has been used for the optimal placement of various

types of FACTS controllers such as TCSC, TCPAR and SVC

in order to minimize total system reactive power loss and

hence maximizing the static voltage stability in [111]. Magaji

and Mustafa et al. [43] has been suggested a residue factor

approach for optimal location of FACTS devices for damping

oscillations of power systems. S. N. Singh and I. Erlich et al.

[53] proposed for locating UPFC for enhancing power System

loadability.

B. Optimization Based Techniques

This section reviews the optimal placement of FACTS

controllers based on various optimization techniques such as a

linear and quadratic programming, non-linear optimization

programming, integer and mixed integer optimization

programming, and dynamic optimization programming.

1. Non-Linear Optimization Programming (NLP) techniques

When the objective function and the constraints are non-linear,

it forms non-linear programming (NLP). The difference

between NLP and Linear Programming (LP) is analogous to

the difference between a set of solving non-linear equations

and a set of solving linear equations. In most of the NLP

methods, the approach is to start from an initial guess and to

determine a descent direction in which objective function

decreases in case of minimization problems [5].

Reference [38], suggests a non-linear optimization

programming techniques for assessing the placement of

FACTS controllers in power system to damp electro-

mechanical oscillations. A non-linear optimization

programming techniques has been proposed for optimal

network placement of SVC controller in [39] and a Benders

Decomposition technique has been used for these solutions.

2. Integer and Mixed –Integer optimization Programming

(IP & MIP) techniques

Reference [40], a mixed integer linear optimization

programming algorithm has been proposed for the optimal

placement of TCPST in multi-machine power systems. A

mixed integer optimization programming algorithm has been

proposed for optimal placement of Thyristor Controlled Phase

Shifter Transformers (TCPSTs) in large scale power system

for active flow and generation limits, and phase shifter

constraints in [41]. A mixed integer optimization

programming algorithm has been proposed for allocation of

FACTS controllers in power system for security enhancement

against voltage collapse and corrective controls, where the

control effects by the devices to be installed are evaluated

together with the other controls such as load shedding in

contingencies to compute an optimal VAR planning [42]. In

[100], a mixed integer non-linear optimization programming

algorithm is used for determine the type, optimal number,

optimal location of the TCSC for loadability enhancement in

deregulated electricity markets. A mixed integer optimization

programming algorithm has been used for optimal location of

TCSC in a power system in [101].

3. Dynamic Programming (DP) techniques

Oliveira et al. suggested a dynamic optimization programming

algorithm for allocation of FACTS devices in hydrothermal

systems in order to minimize the expected thermal generation

costs and the investments on FACTS devices in a pre-

specified time interval [44]. Chang and Huang et al. showed

that a hybrid optimization programming algorithm for optimal

placement of SVC for voltage stability reinforcement [45]. In

[46], a bellmann’s optimality principle has been proposed for

optimal sectionalizing switches allocation in distribution

networks and also determines the optimal number of

automatic sectionalizing switches devices. Lie and Deng et al.

has addressed a decomposition coordination technique for

optimal FACTS devices allocation in power system economic

dispatch [47]. Zuwei and Lusan et al. presented review on the

current status on the optimal placements of FACTS devices in

power systems for enhances the damping of power system

oscillations [48]. Orfanogianni and Bacher et al. suggested an

optimization-based methodology is used for identify key

locations of TCSC and UPFC include the nonlinear constraints

of voltage limitation, zero megawatt active power exchange,

voltage control, and reactive power exchange in the ac

networks [103]. In [104], Curved Space Optimization (CSO)

programming algorithm is used for allocation of SVC in a

large power system.

C. Artificial Intelligence (AI) Based Techniques

This section reviews the optimal placement of FACTS

controllers based on various Artificial Intelligence based

techniques such as a Genetic Algorithm (GA), Expert System

(ES), Artificial Neural Network (ANN), Tabu Search

Optimization (TSO), Ant Colony Optimization (ACO)

algorithm, Simulated Annealing (SA) approach, Particle

Swarm Optimization (PSO) algorithm and Fuzzy Logic based

approach.

1. Genetic Algorithm(GA)

A genetic algorithm has been addressed for optimal location of

phase shifters in the French network to reduce the flows in

heavily loaded lines, resulting in an increased loadability of

the network and a reduced cost of production [49]. A genetic

algorithm has been addressed for optimal location of multiple


COS0205-4

type FACTS controllers in a power system. The optimization

are performed on three parameters; the location of the devices,

their types and their values. The system loadability is applied

as measure of power system performance. Four different kinds

of FACTS controllers are used as models for steady state

studies: TCSC, TCPST, Thyristor Controlled Voltage

Regulator (TCVR) and SVC in order to minimizing the overall

system cost, which comprises of generation cost and

investment cost of FACTS controllers [50]. Vijakumar and

Kumudinidevi et al. presented a novel method for optimal

location of FACTS controllers in a multi-machine power

system. The location of FACTS controllers, their type and

rated values are optimized simultaneously for the various

FACTS controllers, TCSC and UPFC are considered in order

to minimizing the overall system cost, which comprises of

generation cost and investment cost of FACTS controllers

[51]. A stochastic searching algorithm called as genetic

algorithm has been proposed for optimal placement of static

VAR compensator for enhancing voltage stability in [52]. In

[54], a genetic algorithm (GA) based method has been

proposed for determine the optimal placement of FACTS

controllers in power system with the consideration of

economics and cost effectiveness. In [55], a genetic algorithm

(GA) based approach has been proposed for the optimal

choice and allocation of FACTS devices in deregulated

electricity power market is to achieve the power system

economic generation allocation and dispatch in deregulated

electricity market. The locations of the FACTS controllers,

their type and ratings are optimized simultaneously. Reference

[56], genetic algorithm (GA) and particle swarm optimization

(PSO) has been proposed for optimal location and parameter

setting of UPFC for enhancing power system security under

single contingencies. A new genetic algorithm (GA) based

approach [57] has been addressed for selection of the optimal

number and location of UPFC devices in deregulated electric

power systems. In [58], a novel method such as a genetic

algorithm has been presented for optimal location of FACTS

controllers in a multi-machine power system. The location of

FACTS controllers, their type and rated values are optimized

simultaneously for the various FACTS controllers such as a

TCSC and UPFC are considered. Reference [59], a genetic

algorithm (GA) has been proposed for location and parameters

setting of UPFC for congestion management in pool market

model. The planning method such as a micro-genetic

algorithm (MGA) has been addressed for optimal type

selection and placement of a FACTS device for power system

stabilizing purpose in a multi-machine power system [60]. A

heuristic approach using genetic algorithm has been addressed

for optimal location of FACTS controllers in multi-machine

power systems for enhancing the damping of power system

oscillations in [61]-[64]. In [105], a non-traditional

optimization technique, a Genetic Algorithm (GA) is

conjunction with Fuzzy logic (FL) is used to optimize the

various process parameters involved in introduction of FACTS

devices such as a TCSC, Thyristor Controlled Phase Angle

Regulator (TCPAR), SVC and UPFC in a power system. The

various parameters taken into consideration were the location

of the device, their type, and their rated value of the devices. A

multi-objective optimal power flow and genetic algorithms

used to determine the optimal number and location of UPFC

devices in an assigned power system network for maximizing

system capabilities, social welfare and to satisfy contractual

requirements in an open market power [106]. An energy

approach has been used for the optimal location of FACTS

controllers/sensors in large-scale power systems in [109].

Reference [110], a genetic algorithm (GA) has been proposed

for optimal choice and allocation of FACTS devices such as

UPFC, TCSC, TCPST, and SVC in deregulated electricity

market.

2. Tabu Search Algorithm (TS)

A TS algorithm has been addressed for optimal placement of

FACTS controllers such as TCSC, TCPST, UPFC, and SVC

in multi-machine power systems [65]-[66]. Reference [102], a

hybrid-meta heuristic method based on tabu search in

conjunction with evolutionary particle swarm optimization

technique has been proposed for optimal location of UPFC in

power system.

3. Simulated Annealing (SA) Algorithms

References [69], [70], a novel optimization based

methodology such as a simulated annealing has been

proposed for optimal location of FACTS devices such as

TCSC and SVC in order to relive congestion in the

transmission line while increasing static security margin and

voltage profile of power system networks. In [107], the Goal

Attainment (GA) method based on the SA approach is applied

to solving general multi-objective VAR planning problems by

assuming that the Decision Maker (DM) has goals for each of

the objective functions. The VAR planning problem involves

the determination of location and sizes of new compensators

considering contingencies and voltage collapse problems in a

power system.

4. Particle Swarm Optimization (PSO) Algorithms

In [71], a Particle Swarm Optimization (PSO) algorithm has

been addressed for the solution of the Optimal Power Flow

(OPF) using controllable FACTS controllers to enhance

economic solution. Rashed et al. suggested a Genetic

Algorithm (GA) and PSO techniques for optimal location and

parameter setting of TCSC to improve the power transfer

capability, reduce active power losses, improve stabilities of

the power network, and decrease the cost of power production

and to fulfill the other control requirements by controlling the

power flow in multi-machine power system network [72]. A

Trinary Particle Swarm Optimization Technique has been

proposed for optimal switch placement in distribution systems

for achieving high distribution reliability levels and con-

currently minimizing capital costs can be considered as the

main issues. A novel three state approaches has been proposed

for inspired from the discrete version of a powerful heuristic

algorithm, PSO is developed and presented to determine the

optimal number and locations of two types of switches


COS0205-5

(sectionalizes and breakers) in radial power systems

automation is an important issue from the reliability and

economical point of view [73]. In [74]-[76], an Artificial

Intelligence Based Techniques has been addressed for optimal

placement of FACTS controllers in large scale power system.

In [80], a Particle Swarm Optimization (PSO) technique has

been addressed for optimal location of FACTS controllers

such as TCSC, SVC, and UPFC considering system

loadability and cost of installation.

5. Ant Colony Optimization (ACO) algorithms

In [77], a methodology has been suggested for placement of

sectionalizing switches in distribution networks in the

presence of distributed generation sources for reliability

improvement with consideration of economic aspects. In [78],

an ACO algorithm has been addressed for the planning

problem of electrical power distribution networks, stated as a

mixed non-linear integer optimization problem, is solved

using the Ant Colony System (ACS) algorithm. The ACS

methodology is coupled with a conventional distribution

system load flow algorithm and adapted to solve the primary

distribution system planning problem. A Graph Search

Algorithm has been addressed for optimal placement of fixed

and switched capacitors on radial distribution systems to

reduce power and energy losses, increases the available

capacity of the feeders, and improves the feeder voltage

profile [79].

6. Fuzzy Logic (FL) Algorithms

References [81]-[82], A fuzzy logic based approach has been

addressed for optimal placement and sizing of FACTS

controllers in power systems. In [83], the theory of the normal

forms of diffeomorphism algorithm has been addressed for the

SVC allocation in multi-machine power system for power

system voltage stability enhancement. Luna and Maldonado et

al. has been addressed a new methodology is based on the

evolutionary strategies algorithm known as Evolution

Strategies (ES) for optimally locating FACTS controllers in a

power system for maximizes the system loadability while

keeping the power system operating within appropriate

security limits [84]. A Gravitational Optimization (GO)

algorithm has been addressed for allocation of SVC in a power

system in [85]. Kalyani et al. [86] has been suggested an

Evolutionary algorithm for optimal location of UPFC and

sequential quadratic programming (SQP) to optimize the

UPFC control settings. In [108], a knowledge and algorithm

based approach is used to VAR planning in a transmission

system. The VAR planning problem involves the

determination of location and sizes of new compensators

considering contingencies and voltage collapse problems in a

power system. Fang and Ngan et al. [67] suggested an

augmented Lagrange Multipliers approach for optimal

location of UPFC in power systems to enhances the steady

state performance and significantly increase the loadability of

the system. Hao et al. [68] has been proposed an improved

evolutionary programming technique for optimal location and

parameters settings of UPFCs to maximize the system

laudability subject to the transmission line capability and

specified voltage level.

III. SUMMARY OF THE PAPER

The following tables give summary of the paper as:

A. Methods/Techniques for Placement of FACTS

controllers

1. Methods/Techniques point of view

Methods/Techniques Total No. of

Literatures Reviews out of

106 Literatures

% of Literatures

Reviews out of 106 Literatures

Sensitivity based methods 48 45.28

Optimization based methods 14 13.20

AI-based techniques 44 41.51

2. Operating Parameters point of view

Operating Parameters of Power

systems Total No. of Literatures

Reviews out of

106 Literatures

% of Literatures Reviews out of

106 Literatures

Damping of power system

oscillations

16 15.09

Voltage Profile 20 18.87

Security of the power system 02 1.89

Small signal stability, transient stability

06 5.66

Power transfer capability

through the lines

02 1.89

Dynamic performances of

power systems

02 1.89

The loadability of the power

system network

12 11.32

Others parameters point of view 46 43.39

From above tables it is concluded that the 45.28% of total

literatures are reviews based on sensitivity methods, 13.20%

of total literatures are reviews based on optimization

programming and the 41.51% of total literatures are reviews

on AI based techniques regarding with placement of FACTS

controllers in multi-machine power systems. It is also

concludes that the 15.09% of total literatures are reviews

carryout from damping of power system oscillations, 18.87%

of total literatures are reviews carryout from voltage stability,

1.89% of total literatures are reviews carryout from security of

power system, 5.66% of total literatures are reviews carryout

from small signal/transient/dynamic stability, 1.89% of total

literatures are reviews carryout from power transfer capability

through the lines, 1.89% of total literatures are reviews

carryout from dynamic performance of power system, 11.32%

of total literatures are reviews carryout from the loadability of

power system, and 43.39% of total literatures are reviews

carryout from other parameters viewpoints.

Finally it is concluded that the maximum research work

carryout from damping of power system oscillations and


COS0205-6

voltage stability point of view regarding with placement of

FACTS controllers in multi-machine power systems.

IV. CONCLUSIONS

In this paper an attempt has been made to reviews, various AI

based optimization methods used for the placement of FACTS

controllers. Even through, excellent advancements have been

made in classical methods i. e. sensitivity based method, they

suffer with the following disadvantages: In most cases,

mathematical formulations have to be simplified to get the

solutions because of the extremely limited capability to solve

real-word large-scale power system problems. They are weak

in handling qualitative constraints. They have poor

convergence, may get stuck at local optimum, they can find

only a single optimized solution in a single simulation run,

they become too slow if number of variables are large and

they are computationally expensive for solution of a large

power system.

Whereas, the major advantage of the AI based optimization

methods is that they are relatively versatile for handling

various qualitative constraints. AI methods can find multiple

optimal solutions in single simulation run. So they are quite

suitable in solving multi-objective optimization problems for

placement FACTS controllers in multi-machine power system.

In most cases, they can find the global optimum solution. The

main advantages of ANN are: possesses learning ability, fast,

appropriate for non-linear modeling, etc. whereas, large

dimensionality and the choice of training methodology are

some disadvantages of ANN. The advantages of Fuzzy

method are: Accurately represents the operational constraints

and fuzzified constraints are softer than traditional constraints.

The advantages of GA methods are: It only uses the values of

the objective function and less likely to get trapped at a local

optimum. Higher computational time is its disadvantage. The

advantages of EP are adaptability to change, ability to

generate good enough solutions and rapid convergence. ACO

and PSO are the latest entry in the field of optimization. The

main advantages of ACO are positive feedback for recovery of

good solutions, distributed computation, which avoids

premature convergence. It has been mainly used in finding the

shortest route in transmission network, short-term generation

scheduling and optimal unit commitment. PSO can be used to

solve complex optimization problems, which are non-linear,

non-differentiable and multi-model. The main merits of PSO

are its fast convergence speed and it can be realized simply for

less parameters need adjusting. PSO has been mainly used to

solve bi-objective generation scheduling, optimal reactive

power dispatch and to minimize total cost of power

generation. The applications of ACO and PSO for placement

of FACTS controllers in multi-machine power system.

This paper has also addressed a survey of several technical

literature concerned with various techniques/methods for

placement FACTS controllers in multi-machine power system

environments to show that the achieve significant

improvements in operating parameters of the power systems

such as small signal stability, transient stability, damping of

power system oscillations, security of the power system, less

active power loss, voltage profile, congestion management,

quality of the power system, efficiency of power system

operations, power transfer capability through the lines,

dynamic performances of power systems, and the loadability

of the power system network also increased. This review also

shows that installing multiple controllers on the system may

not improve the dynamic performance due to undesirable

interactions. The tuning of one controller may affect other

controllers and thus lead to unstable conditions. These issues

should be taken into consideration when designing systems

with multiple controllers. The implementation of a

coordinated controller tuning procedure to avoid undesirable

interactions in power systems, and thus improve overall

dynamic performance is under this review.

Authors strongly believe that this survey article will be very

much useful to the researchers for finding out the relevant

references as well as the previous work done in the field of

placement of FACTS Controllers for the various

methods/techniques for placement of FACTS controllers in

multi-machine power systems. So that further research work

can be carried out.

ACKNOWLEDGMENT

The authors would like to thanks Dr. S. C. Srivastava, and Dr.

S. N. Singh, Indian Institute of Technology, Kanpur, U.P.,

India, and Dr. K.S. Verma, and Dr. Deependra Singh, Kamla

Nehru Institute of Technology, Sultanpur, U.P., India, for their

valuables suggestions regarding placement and coordination

techniques for FACTS controllers form voltage stability, and

voltage security point of view in multi-machine power

systems environments.

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[96] L. Rouco, and F.L. Pagola,“An Eigen-value Sensitivity Approach to

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Systems, Vol.12, No.4, November1997.

[97] J. G. Singh, S. N. Singh, and S. C. Srivastava, “Optimal Placement of Unified Power Flow Controller Based on System Loading

Distribution Factors,” Electric Power Components and Systems, Vol.

37, Issue 4, April 2009, pp.441-463 [98] Nuraddeen Magaji, and M. W. Mustafa, “Optimal Location of TCSC

Device for Damping Oscillations,” ARPN Journal of Engineering and

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Compensator Device for Damping Oscillations,” American Journal of

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Loadability Enhancement in Deregulated Electricity Markets Using

MINLP,” International Journal of Emerging Electric Power Systems, Vol.5, Issue 1, 2006.

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Dong, “TCSC Allocation Based on Line Flow Based Equations Via Mixed-Integer Programming,” IEEE Trans on Power Systems,

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controllers,” Ph. D. Thesis, IIT Kanpur, October, 2000.

BIOGRAPHIES

Bindeshwar Singh was born in Deoria, U.P., India, in 1975. He received the B.E. degree in electrical engineering from the Deen Dayal of University of Gorakhpur, Gorakhpur, U.P., India, in 1999, and M. Tech.

in electrical engineering (Power Systems) from the Indian Institute of

Technology (IITR), Roorkee, Uttaranchal, India, in 2001. He is now a Ph. D.

student at Uttar Pradesh Technical University, Lucknow, U.P., India. In 2001, he joined the Department of Electrical Engineering, Madan Mohan Malviya Engineering College, Gorakhpur, as an Adoc. Lecturer. In 2002, he joined the Department of Electrical Engineering, Dr. Kedar Nath Modi Institute of Engineering & Technology, Modinagar, Ghaziabad, U.P., India, as a Sr. Lecturer and subsequently became an Asst. Prof. & Head in 2003. In 2007, he joined the Department of Electrical & Electronics Engineering, Krishna Engineering College, Ghaziabad, U.P., India, as an Asst. Prof. and subsequently became an Associate Professor in 2008. Presently, he is an Assistant Professor with Department of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur, U.P., India, where he has been since August’2009.

His research interests are in Placement and Coordination of FACTS

controllers in multi-machine power systems and Power system Engg. Mobile: 09473795769, 09453503148 Email:[email protected]

,[email protected]

Nikhlesh Kumar Sharma received the Ph.D. in electrical engineering from

the Indian Institute of Technology, Kanpur, in 2001. Currently, he is a Director with, Raj Kumar Goel Engineering College, Pilkhuwa, Ghaziabad, U.P., India,

where he has been since June’2009. His interests are in the areas of FACTS control and Power systems.



COS0205-10

Mobile: 09654720667, 09219532281

Email: [email protected]

A.N.Tiwari received the Ph.D. in electrical engineering from the Indian

Institute of Technology, Roorkee, in 2004. Currently, he is an Asst. Prof. with

Department of Electrical Engineering, Madan Mohan Malviya Engineering College, Gorakhpur,U.P., India, where he has been since June’1998. His

interests are in the areas of Electrical Drives and Application of Power

Electronics. Mobile: 09451215400

Email:[email protected]

1

Praveen Kumar Shukla*, Surya Prakash Tripathi**

Abstract – In real world computing environment, the information is not complete, precise and certain, making very difficult to derive an actual decision. To deal with processing and modeling of such information, fuzzy techniques are applied to exercise the proper conclusion. This paper focuses on the basics of Fuzzy Logic and its application in Rule Based Systems to make them capable to handle the real world problems. Also, different research issues associated with FRBSs have been discussed.

Keywords – Fuzzy Logic (FL), Fuzzy Sets, Linguistic Variables, Control Systems, Fuzzy Rule Based Systems (FRBS).

I. INTRODUCTION Humans are capable to use linguistic information

precisely in their decision making. Due to imprecise and uncertain nature of the linguistic information, machines are not capable to use them in decision making processes using traditional methods. To make the machines intelligent, like humans in this regard, Fuzzy Techniques are used.

The idea of the Fuzzy Logic was first introduced by Professor Lotfi Ahmad Zadeh, at University of Berkeley, California in his seminal paper “Fuzzy Sets” [1].

Fuzzy Logic [2, 3] is a form of multi-valued logic derived from fuzzy set theory to deal with approximate reasoning. It provides the means to represent and process the linguistic information and subjective attributes of the real world. Fuzzy Logic is the extension of Boolean Crisp Logic to deal with the concept of partial truth. Fuzzy Logic is applied in the number of areas, i.e. engineering applications, medical applications, economics and management, industrial applications and many more. It is also integrated with other soft computing techniques, like Neural Network (an approach that mimics the functionality of human brain), Genetic Algorithms (a nature inspired search and optimization technique), PSO (Particle Swarm Optimization) etc. In the early stage of the Fuzzy Logic, a number of misconceptions have been created. Here we are going to introduce few points about fuzzy logic to make the concept very clear. 1. Fuzzy logic is not fuzzy. 2. Fuzzy logic is precise. 3. Fuzzy Logic is a precise system of reasoning, deduction and computation in which the objects of

discourse and analysis are associated with information ,which is or is allowed to be imperfect. 4. Any formal system can be fuzzified. Rule Base Systems [4] are highly applicable in decision making, control systems and forecasting. To deal with imprecise, uncertain and inexact real world knowledge, in rule based systems, fuzzy techniques are used. Fuzzy logic is the way to represent the complex situations in terms of simple natural languages. This paper introduces the Fuzzy Rule Based Systems (FRBS) and different research issues associated with them. In section II, the basic mathematical concept of the Fuzzy Logic has been introduced. Section III introduces the two basic types of FRBS, Mamdani FRBS and TSK FRBS. In section IV, authors revisited five research issues with the Fuzzy Rule Based Systems (FRBS). In section V, conclusion and future scope of the research issues ha been discussed.

II. BASIC CONCEPTS: FUZZY LOGIC

The theory of Fuzzy Logic [5] can be developed using the concepts of Fuzzy Sets similar to how theory of crisp bivalent logic can be developed using the concept of crisp sets. Specifically, there exists an isomorphism between sets and logic. In view of this, a good foundation of the fuzzy sets is necessary to develop the theory of Fuzzy Logic.

A fuzzy set is a set without clear or sharp (crisp) boundaries. Partial membership degree is possible in fuzzy sets. In other words, softness is associated with the membership of elements associated.

Examples may include, like TEMPERATURE. This fuzzy variable may take fuzzy values, like COLD, COOL, WARM, HOT. A fuzzy value such as ‘WARM’ is a fuzzy descriptor.

A. Universe of Discourse

If universe of discourse is represented by X, is a set that contains every set of interest in the context of a given class of a problem. The vein diagram of the fuzzy set is given in Fig. 1.

Fig. 1 Venn diagram of Fuzzy Set

Fuzzy Logic and Rule Based Systems: Research Issues & Challenges

* Senior Lecturer, Department of Information Technology, Northern India Engineering College, Lucknow ([email protected])

**Associate Professor, Department of Computer Science &

Engineering, Institute of Engineering & Technology (A Constituent College of GBTU), Lucknow ([email protected])

2

B. Representation of Fuzzy Sets There are two representation techniques of fuzzy sets,

membership function method and symbolic representation. Membership Function Method

This function gives the grade (degree) of membership within the set of any element of Universe of Discourse. The membership function maps the elements of the universe on to the numerical values in the interval [0, 1].

]1,0[:)( XxA Here, )(xA is the membership function of the fuzzy

set A in the universe X. It is defined as follows: ]1,0[)(,));(,( xAXxxAxA

The membership function represents the grade of possibility that an element x belongs to the set A. It is a possibility function, not a probability function.

Symbolic Representation

A fuzzy set may be symbolically represented as follows: )(| xAxA . They can also be represented as a formal series, when the universe is discrete in the nature.

....................../)1(

...............2/)2(1/)1(

ixixA

xxAxxAA

or

Xix ix

ixAA)(

If the universe is continuous then it can be represented as follows:

Xx x

xAA)(

C. Algebraic Operations on Fuzzy Sets

Let X is a set of objects with elements denoted by x, i.e. X=x

A fuzzy set A in X is characterized by a membership function )(xA , which maps each point in X on to the real interval [0.0, 1.0]. As )(xA approaches 1.0, the grade of membership of X in A increases.

1. A is empty iff for all x, )(xA =0.0 2. If A and B are the two fuzzy sets, then A=B iff for

all x: )(xA = )(xb .

3. )'(xA =1- )(xA

4. A is contained in B iff )(xA <= )(xb 5. AUNIONBBAC , where

))(),(max()( xbxaxC 6. IONBAINTERSECTBAC , where

))(),(min()( xbxaxC

D. Support Set This is a crisp set of a fuzzy set containing all the

elements (in the universe) whose membership grade is greater than 0. The support set S of a fuzzy set A with membership function )(xA is given by

0)(| xAXxS

Fig. 2 Fuzzy Membership Functions E. -cut of the fuzzy set

The -cut of the fuzzy set A is the crisp set denoted by A formed by those elements of A whose membership function grade is greater than or equal to a specified threshold value .

]1,0[,)(| xAXxA The strong -cut is defined by

]1,0[,)(| xAXxA When =0 then -cut will become the support set of

a fuzzy set. Also, the fuzzy AND, OR and NOT operations can be

generalized. The generalized FUZZY AND operation is called Triangular Norm (T-Norm) and generalized FUZZY UNION is called T-Conorm (S- Norm). The basic concepts of Fuzzy Set Theory may be studied in [3, 5].

III. FUZZY RULE BASED SYSTENS (FRBS)

Fuzzy Rule Based Systems (FRBS) constitute an

extension to classical rule based systems, because they deal with IF – THEN rules whose antecedents and consequents are composed of fuzzy logic statements, in place classical logical ones.

The most common applications of FRBS includes, Fuzzy Modeling [6], Fuzzy Control [7] and Fuzzy Classification [8]. In a FRBS, fuzzy logic used to perform the operations like, representation of different form of knowledge, modeling the interactions and relationships that exist among its variables. The main features of the knowledge captured by fuzzy sets involve handling of uncertainty. Due to this, inference methods have become more robust and flexible with the approximate reasoning methods using Fuzzy Logic.

Linguistic variables and values are used for the enhancement of the Knowledge Representation. These variables and their values are defined by the context dependent fuzzy sets whose meanings are specified by gradual membership function.

Two major types of FRBSs proposed are, Mamdani Fuzzy Rule Based Systems [9] and Takagi-Sugeno-Kang FRBSs [10]. A. Mamdani Fuzzy Rule – Based Systems

These are the FRBS with fuzzifier and defuzzifier, more commonly these are known as Fuzzy Logic

3

Controllers (FLCc). The major constituents of the Mamdani FRBS are shown in Figure 3.

Fig. 3 Mamdani Fuzzy Rule Based Systems

1. Knowledge Base: The Knowledge Base (KB) stores the available knowledge about the problem in the form of fuzzy “IF THEN” rules. It composed of two main components, Data Base (DB) and Rule Base (RB). Data Base (DB) stores the membership functions of fuzzy sets and scaling functions for context adaptation purpose. Rule Base (RB) stores the FUZZY IF THEN rules for the purpose inference and decision making. Multiple rules can be fired simultaneously for the same input.

2. Fuzzification Interface: It transforms the crisp input data into fuzzy values that acts as input to fuzzy reasoning process.

3. Inference System: It infers from the fuzzy input to several resulting output fuzzy sets according to the information stored in the Knowledge Base (KB).

4. Defuzzification Interface: It converts the fuzzy sets obtained from the inference process into a crisp action that constitutes the global output of the FRBS.

B. TSK Fuzzy Rule Based Systems

A new FRBS model is proposed, based on rules whose antecedent is composed of the linguistic variables and the consequent is represented by a function of the input variables.

IF X1 is A1 and…………and Xn is An THEN Y=p1.X1+……+pn.Xn+p0

Here, Xi is the system input variable, Y as the output variable p= (p0, p1,…….., pn) is a vector of real parameters. Ai is the direct specification of a fuzzy set or linguistic label that points to a one particular member of a fuzzy partition of a linguistic variable.

The output of a TSK FRBS using a KB composed of m rules is obtained as a weighted sum of the individual outputs provided by each rule, Yi is given by

mi ih

iYmi ih

1

.1

Here, i=1, 2,………,m. ))(.,),........1(1( nxinAxiATih is the matching degree

between the antecedent part of the ith rule and the current

inputs to the systems, ),.......1(0 nxxx . T stands for the conjunctive operator modeled by a t-norm.

Fig. 4 TSK Type Fuzzy Rule Based Systems

IV. RESEARCH ISSUES AND CHALLENGES

Application of fuzzy computing in rule based systems

provides a mathematical platform to deal with information imprecision in decision making processes. Several issues have been raised by the researchers in this field. We are presenting here the burning issues in the FRBS research. A. Context Adaptation

Human decisions and perceptions are extremely dependent on the context. Therefore, when Fuzzy Logic models the human decision making capabilities in machines, it is mandatory to apply context in Fuzzy Systems.

Context adaptation in Fuzzy Systems has been approached as scaling of fuzzy sets from one universe of discourse to another by means of non-linear scaling functions, whose parameters are identified by the data.

The idea behind this research issue is to develop new context aware approaches to automatic development of the fuzzy systems from data. Other objectives includes, interpretability oriented adaptation, identification of context variable, high level of rule identification.

The abstract idea of this research issue [11] is given in Fig. 4.

Fig. 5 Context Adaptation Process In [12], a method of achieving context adaptation by adjusting an initial normalized fuzzy rule based systems through the use of operators that appropriately change the representation of linguistic variables. A multi-objective evolutionary algorithm based Pareto-optimum context adapted Mamdani Type FRBSs with different trade – offs between accuracy and interpretability is investigated in [13]. In this proposal, a

4

novel index is proposed based on fuzzy ordering relations, in-order to provide a measure of interpretability. Other approaches related to context adaptation can be studied in [14, 15, 16, 17, 18]. B. Interpretability Accuracy Trade-Off

The trade – off between interpretability and accuracy is an important research issue. The definition of the accuracy is straight forward in different applications, but the definition of the interpretability is rather problematic [19].

The main purpose is to build fuzzy systems with a user controllable trade off between accuracy and interpretability. Interpretability is maintained by the structural choices regarding the type of membership functions, rules and inference mechanism. Interpretability can be maintained or enhanced during the fuzzy systems generation or obtained by post processing of the resulting data driven fuzzy systems [20, 21].

A new linguistic rule representation model [22,23] was proposed to perform a genetic lateral tuning of membership functions, based on linguistic 2- tuple representation model that allow the lateral displacement of a label considering a unique parameter. It provides reduction of search space that eases the derivation of optimal path.

A user-controllable interpretability-accuracy trade off for fuzzy systems has been discussed in [24].

The interpretability- accuracy trade off issue is discussed with multi-objective fuzzy genetics-based machine learning in [25].

C. Fuzzy Rule Selection

In high dimensional data problems [26, 27], the number of rules in the Rule Base grows exponentially as more inputs are added. Hence, it is required to have a fuzzy rule generation method. It is likely to derive fuzzy rule sets, including following types of rules; Redundant rules: The actions of these rules are covered by the other rules. Wrong rules: These are ill defined rules and perturb the systems performance. Conflicting rules: These rules worsen the system performance, when co-existing with other rules in the RB.

Other solutions for the problem of data dimensionality and rule overflow, are rule reduction methods. Two approaches are proposed for this rule reduction:

Approach 1: To combine the membership functions of two or more rules, reducing to a single one’s (Scatter partition FRBS).

Approach 2: To select the fuzzy rules, we get the rule subsets with a good cooperation from the initial RB (descriptive and scatter FRBS).

Several methods can be obtained from selection, with different search algorithms that are providing most successful combination of fuzzy rules [28,29,30].

A genetic rule selection process in order to obtain a compact and accurate fuzzy rule based classification systems is discussed in [31].

D. Optimization of membership function and scaling function

Optimization of the membership function results in the improvement of interpretability and accuracy of the fuzzy systems. Also, the scaling functions are optimized to maintain and precise the context related issues in the fuzzy rule based systems. Scaling functions apply the universe of discourse of the input and output variables to the domain where fuzzy sets are defined. Their tuning and adaptation allows the scaled universe of discourse to match the variable range in a better way. Several parameters are considered for the purpose of optimization. These may include, 1. Scaling Functions, 2. Upper and lower bounds (Linear Scaling Functions), 3. Contraction/dilation operators (Nonlinear scaling functions).

Also the optimization of the membership functions is an important research issue. The tuning process [32] slightly adjusts the shapes of the membership functions of the preliminary data base definition. Different types of membership functions are considered for this purpose, i.e. 1. Triangular, 2. Trapezoidal, etc.

For the purpose of semantic interpretability of linguistic fuzzy models, an index is proposed [33]. Tuning of the membership function and rule selection is performed using a multi-objective evolutionary algorithms.

E. Fuzzy Partition Granularity

Obtaining good uniform fuzzy partition granularity [32] that improves the FRBS behavior is an important research issue. The granularity selection plays an important role in many characteristics of the FRBS, such as the accuracy in fuzzy modeling or the smoothening in fuzzy control. Also, the granularity of the input variables specifies the maximum number of fuzzy rules that may compose the Rule Base (RB). So, it has a strong influence on aspects, like complexity of rule learning, interpretability of the FRBS obtained or its accuracy.

The issue of the fuzzy partition granularity is a fuzzy rule base classification systems are discussed in [35].

V. CONCLUSION & FUTURE SCOPE

Modern engineering, medical and business applications

are requiring to enhance their capability to deal with imprecise and uncertain information, enabling them to have a strong reasoning and decision power. It makes them to handle more complex and linguistic computations easily and efficiently. All these requirements lead to rapid development and integration of Fuzzy Logic in control systems.

In future, authors would like to implement the solutions for the problems addressed in the section IV, by Evolutionary Computation techniques. The use of multi-objective Evolutionary Algorithms and Memetic Genetic Algorithms will be preferred.

REFERENCES

[1] L. A. Zadeh, “Fuzzy Sets”, Information and Control, Vol. 8, pp.

338-353, 1965. [2] L. A. Zadeh, “Fuzzy sets as a basis of possibility”, Fuzzy Sets

Systems, Vol. 1, pp. 3-28, 1978. [3] T. J. Ross, “Fuzzy Logic with Engineering Applications”,

McGraw-Hill, 1995.

5

[4] L. M. Pant, A. Ganju, “Fuzzy Rule Based Systems for prediction of direct action avalanche”, Current Science, Vol. 87, No.1, July, 2004.

[5] F. O. Karray, C. De Silva, “Soft Computing and Intelligent Systems Design-Theory, Tools and Applications”, Pearson Publications, 2004.

[6] W. Pedrycz (Eds.), Fuzzy Modelling: Paradigms and Practice, Kluwer Academic Press, 1996.

[7] D. Drainkov, H. Hellendorn, M. Reinfrank, An introduction to Fuzzy Control, Springer-Verlag, 1993.

[8] Z. Chi, H. Yan, T. Pham, Fuzzy Algorithms: With applications to image processing and pattern recognition, World – Scientific, 1996.

[9] E. H. Mamdani, S. Assilian, “An experiment in linguistic synthesis with fuzzy logic controllers”, International Journal of Man-Machine Studies, Vol. 7, pp. 1-13, 1975,.

[10] T. Takagi, M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control”, IEEE Transactions Systems, Man and Cybernetics, Vol. 15, No. 1, pp. 116-132.

[11] URL: “http://cig.iet.unipi.it/cig/research02.html” [12] A. Botta, B. Lazzerini, F. Marcelloni, “Context adaptation in

Mamdani Fuzzy Systems through new operators tuned by a genetic algorithms”, FUZZ-IEEE, pp. 1641-1648, 2006.

[13] A. Botta, B. Lazzerini, F. Marcelloni, D. C. Stefanescu, “Context adaptation of fuzzy systems through a multi objective evolutionary approach based on a novel interpretability index”, Soft Computing, 2008, pp. 437-449.

[14] A. Botta, B. Lazzerini, F. Marcelloni, “Context adaptation of Mamdani fuzzy rule based systems”, International Journal of Intelligent Systems, Vol. 23, No. 4, pp. 397-418, 2008

[15] W. Pedrycz, R. R. Gudwin, F. A. C. Gomide, “Non linear context adaptation in the calibration of fuzzy sets”, Fuzzy Sets and Systems, Vol. 88, No 1, pp. 91-97, 1997.

[16] L. Magdalena, “On the role of context in hierarchal fuzzy controllers”, International Journal of Intelligent Systems, Vol. 17, No. 5, pp. 471-493, 2002.

[17] R. R. Gudwin, F. A. C. Gomide, W. Pedrycz, “Context adaptation in fuzzy processing and genetic algorithms”, International Journal of Intelligent Systems, Vol. 13, No. 10-11, pp. 929-948, 1998.

[18]O. Cordon, F. Herrera, L. Magdalena, P. Villar, “A genetic learning process for the scaling factors, granularity and contexts of the fuzzy rule based systems data base”, Information Science, Vol. 136 (1-4), pp. 85-107, 2001.

[19] R. Mikut, J. Jakel, L. Grall, “Interpretability issues in data based learning of the fuzzy systems”, Fuzzy Sets and Systems, Vol. 150, pp. 179-197, 2005.

[20] U. Bodenhofer, P. Bauer, A formal model of interpretability of linguistic variables, in: J. Cassilas, O. Cordon, F. Herrera, L. Magdalena (Eds.), Trade off between accuracy and interpretability in fuzzy rule based modeling, Studies in Fuzziness and Soft Computing, Physica, Heidelberg, 2002.

[21] O. Cordon, F. Herrera, “A proposal for improving the accuracy of the linguistic modeling”, IEEE Transactions on Fuzzy Systems, Vol. 8, No. 3, pp. 335-344, 2000.

[22] R. Alcala, J-Alcala-Fdez, F. Herrera, J. Otero, “Genetic learning of knowledge bases of a fuzzy system by using the linguistic 2-tuple representation”, FUZZ-IEEE, pp. 797-802, 2005.

[23] R. Alcala, J-Alcala-Fdez, F. Herrera, J. Otero, “Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuple representation”, International Journal of Approximate Reasoning, Vol. 44, pp. 45-64, 2007.

[24] R. Mikut, J. Jakel, L. Groll, “Interpretability issues in data-based learning of fuzzy systems”, Fuzzy Sets and Systems, Vol. 150, pp. 179-197, 2005.

[25] H. Ishibuchi, Y. Nojima, “Analysis of interpretability-accuracy trade off of fuzzy systems by multi-objective fuzzy genetics-based machine learning”, International Journal of Approximate Reasoning, Vol. 44, pp. 4-31, 2007.

[26] H. Ishibuchi, K. Nozaki, N. Yamamoto, H. Tanaka, “Selecting fuzzy if-then rules for classification problems using genetic algorithms”, IEEE Transaction on Fuzzy Systems, Vol. 3, No. 3, pp. 260–270, 1995.

[27] O. Cordón, F. Herrera, “A three-stage evolutionary process for learning descriptive and approximate fuzzy logic controller knowledge bases from examples”, International Journal of Approximate Reasoning, Vol. 17, No. 4, pp. 369–407, 1997.

[28] O. Cordon, F. Herrera, “A proposal for improving the accuracy of linguistic modeling”, IEEE Transactions on Fuzzy Systems, Vol. 8, No. 3, pp. 335-344, 2000.

[29] H. Ishibuchi, T. Murata, I. B. Tarksen, “Single objective and two objective genetic algorithms for selecting fuzzy rules for pattern classification problems”, Fuzzy Sets and Systems, Vol. 89, No. 2, pp. 135-150, 1997.

[30] H. Isibuchi, K. Nozaki, N. Yamamoto, H. Tanaka, “Selecting fuzzy if then rules for classification problems using genetic algorithms”, IEEE Transactions on Fuzzy Systems, Vol. 3, No. 3, pp. 260-270, 1995,.

[31] A. Fernandez, M. J. del Jesus, F. Herrera, “Analyzing the hierarchal fuzzy rule based classification systems with genetic rule selection, International workshop on genetic and evolutionary fuzzy systems, Spain, pp. 69-74, March, 2010,.

[32] O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic Fuzzy Systems: Evolutionary Tuning and Learning of Fuzzy Knowledge Bases, Applications in Fuzzy Systems – Applications and Theory, World Scientific, Vol. 19, 2001.

[33] M. J. Gacto, R. Alcala, F. Herrera, “Integration of an index to preserve the semantic interpretability with multi-objective evolutionary rule selection and tuning of linguistic fuzzy systems”, IEEE Transactions on Fuzzy Systems, Vol. 18, No, 3, , pp. 515-531, June 2010.

[34] O. Cordon, F. Herrera, P. Villar, “Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule based systems using simulated annealing”, International Journal of Approximate Reasoning, Vol. 25, pp. 187-215, 2000.

[35] A. Fernandez, S. Garcia, M. J. del Jesus, F. Herrera, “A study of the behavior of linguistic fuzzy rule based classification system in the framework of imbalanced data sets”, Fuzzy Sets and Systems, Vol. 159, pp. 2378-2398, 2008.

Praveen Kumar Shukla is with the department of Information Technology, Northern India Engineering College, Lucknow. He is working here as a Senior Lecturer and is the research scholar in the Department of Computer Science & Engineering, Gautam Buddh Technology University (Formerly UPTU), Lucknow. He is B. Tech. in Information Technology and M. Tech. in Computer Science. He is working on Genetic Fuzzy Systems. His area of interest includes, Computational Intelligence

techniques (Fuzzy Logic, Neural Network, Genetic Algorithms) and other Nature Inspired Artificial Intelligence Techniques. He has published many papers in different National/International Conferences and Journals.

Dr. Surya Prakash Tripathi is with the

Department of Computer Science & Engineering, Institute of Engineering & Technology (A constituent college of GBTU), Lucknow as an Associate Professor. He is M. Tech. from IIT, Delhi and Ph. D. from Lucknow University, Lucknow. He has guided many Ph.D. scholars. His area of interest includes, Databases Operating Systems, Soft Computing Techniques and other Nature Inspired Artificial Intelligence

techniques. He has published many papers in National/International Conferences/ Journals.


COS0301-1

A comparative analysis of controllers controlling uncertain factors affecting the

robust position control of DC motor

Farhad Aslam Gagandeep Kaur

Deptt. of Electrical & Instrumentation Engg. Deptt. of Electrical and Instrumentation Engg.

Thapar University, Patiala. Thapar University, Patiala.

[email protected] [email protected]

ABSTRACT

All the industrial process applications require

robust position control of DC motor . The aim

of this paper is to design a robust position

control of DC motor by selecting different

controllers like P, PI, PID and their tuning

methods. The model of a DC motor is

considered as a third order system with

incorporating uncertainty. This paper

compares the different kinds of tuning

methods of parameter for PID controller. One

is the controller design by Zeigler and Nichols

method, second is the auto tuning of the

controller in basic design mode and third is in

the extended design mode. It was found that

the proposed PID parameters adjustment in

the basic and extended design mode is far

better than the P, PI and Zeigler and Nichols

method. The proposed method could be

applied to the higher order system also.

Keywords: DC motor, PID tuning, Basic

mode, Extended mode, Robust position

control.

I. INTRODUCTION

Due to its excellent speed control

characteristics , the DC motor has been widely

used in industry even though its maintenance

costs are higher than the other motors like

induction motor, synchronous motor,

brushless dc motor. As a result, robust

position control of DC motor has attracted

considerable research and several method

have evolved. Proportional- Integral-

Derivative (PID) controllers along with their

tuning have been widely used for speed and

position control of DC motor.

This paper endeavors to design a system using

two methods of auto tuning of PID parameters

called Basic design mode and Extended

design mode. Auto tuning is basically used to

tune PID gains automatically in a Simulink

model containing a PID controller block. The

PID tuner allows to achieve a good balance

between performance and robustness. It

automatically computes a linear model of the

plant. The PID tuner considers the plant to be

the combination of all blocks between the PID

controller input and output. Thus, the plant

includes all blocks in the control loop, other

than the controller itself. The main objectives

of PID tuner are closed- loop stability (in

which system output remains bounded for

bounded input), adequate performance (in

which closed- loop system tracks reference

changes and suppresses disturbance as rapidly

as possible) and adequate robustness (the loop

design has enough gain margin and phase

margin to allow for modeling errors or

variations in system dynamics.

Basic design mode of PID tuner refine the

controller design by adjusting response time.

It make the closed- loop response of the

controlled system faster or slower. Extended

design mode of PID tuner refine the controller

design by separately adjust loop bandwidth


COS0301-2

and phase margin. The larger the loop

bandwidth, the faster the controller responds

to changes in the reference or disturbances in

the loop. The larger the phase margin, the

more robust the controller is against modeling

errors or variations in plant dynamics. The

objective of this paper is to show that by

employing the proposed tuning of PID

controllers, an optimization can be achieved.

This can be seen by comparing the result of

the PID tuner against the classically tuned

system.

II. MODELING A DC MOTOR

To be modeling a DC Motor, simple circuit of

its electrical diagram as shown in Fig. 1 is

considered. To be Modeling and Simulate the

DC motor, the following steps are to be made

step by step;

Step1: Represent the DC motor

circuit diagram.

Step2: Represent system equations

Step3: Calculate the Transfer

function

Step4: Convert to model block

Step5: Run the Simulation

Step6: Analysis

A. Closed-Loop System Consideration

To perform the simulation of the system, an

appropriate model needs to be established.

Therefore, a model based on the motor

specifications needs to be obtained. Fig. 1

shows the DC motor circuit with Torque and

Rotor Angle consideration.

Fig. 1 Schematic diagram of a DC motor

B. System Equation

The motor torque T is related to the armature

current, i , by a torque constant K;

T = K i (1)

The generated voltage, ea, is relative to

angular velocity by;

ea = K wm = K dθ/dt (2)

From Fig. 1 we can write the following

equations based on the Newton‟s law

combined with the Kirchhoff‟s law:

J d2θ/dt

2 + b dθ/dt = K i (3)

L di/dt + Ri = V- K dθ/dt (4)

C. Transfer Function

Using the Laplace transform, equations (3)

and (4) can be written as:

J s2 θ(s) + b s θ(s) = K I(s) (5)

L s I(s) + R I(s) = V(s) – K s θ(s) (6)

Where s denotes the Laplace operator. From

(6) we can express I(s):

and substitute it in (5) to obtain:

This equation for the DC motor is shown in

the block diagram in Fig. 2. From equation

(8), the transfer function from the input

voltage, V (s), to the output angle, θ, directly

follows:


COS0301-3

Before any consideration of the above

equations, we must know the constant values

of data, K, J, b, V, L and R. This is very

important to the application of DC motor

which we will be used.

Speed N = 1220 rpm = 127.7 rad/sec

Motor inertia J = 1 Kg.m2 V = 240V

EO = V – i Ra = 230.3 V Power P = EO Ia =

3731 W = 5 HP

If = V/ Rf = 1 where Rf = 240 Ω EO = w L

If = 127.7 rad/sec

Therefore for the max speed rpm of 1220, it

can be calculate the torque constant K;

K = 1.88 N. m/A

By using equation (3), for w = dθ/dt

At the steady state (used as analyzed data),

both I and w are stabilized;

Therefore, the total equivalent damping b can

be chosen the value of;

By calculating and assuming the require data

as above, the following value are assigned to

be used for our desire DC Motor Model.

V=240 V; J= 1 kg.m2; b =0.24 N.m.s;

K =1.88 N. m/A; R =0.6 Ω; and L =0.5 H;

Fig. 2 A closed- loop system that

Representing the DC motor

III. SIMULATION, TESTING AND

RESULTS

There are four cases in which uncertainty in

the form of disturbance and load is

incorporated to the system.

Our Design requirement

Maximum Overshoot = < 2%

Undershoot = 0

Rise time ( tr) = < 7 sec

Settling time ( ts) = < 12 sec

Case1: No Load and No Disturbance

Fig. 3 Process model with P controller


COS0301-4

Fig. 4 System output with P controller

Fig. 5 Process model with PI controller

Fig. 6 System output with PI controller

Fig. 7 Process model with PID controller

Fig. 8 System output with PID controller


(Zeigler and Nichols tuning method)


auto tuning (Basic design mode)


auto tuning (Extended design mode)


COS0301-5

Fig. 12 Step disturbance rejection plot with

PID controller auto tuning (Extended design

mode)

Fig. 13 Open- loop Bode plot with PID

controller auto tuning (Extended design mode)

Controller

types

KP KI KD Rise

time

(tr

in

sec)

Settling

time (ts

in sec)

Over

shoot

(%)

Under

shoot

(%)

Gain

margin(db@

rad/sec)

Phase

margin

(deg@rad/sec)

P 0.5 0 0 6.19 15.6 0.377 30 [email protected] [email protected]

PI 0.5154 0.07252 0 2.68 23.9 25.2 8 [email protected] [email protected]

PID 0.1 0.00001 1.001 70 130 0 0 - -

PID (ZN) 0.0138 0.00123 0.038795 50 1500 65 33 - -

PID(Basic) 0.73195 0.0029102 -1.5315 3.36 14.2 9.85 0 [email protected] [email protected]

PID(Ext.) 0.5247 0.0023067 -1.0011 5.59 8.45 1.62 0 [email protected] [email protected]

Table 1. Comparative analysis of different controllers under no load & no disturbance

Hence after implementation of auto tuning of

PID controller in extended design mode, the

system design requirement is achieved which

is shown in table 1.

Case2: Load and No Disturbance

In this case uncertainty in the form of 2nd

order load is incorporated.




COS0301-6










mode)


COS0301-7



Controller

types

KP KI KD Rise

time

(tr

in

sec)

Settling

time (ts

in sec)

Over

shoot

(%)

Under

shoot

(%)

Gain

margin(db@

rad/sec)

Phase

margin

(deg@rad/sec)

P 0.43695 0 0 9.6 16.1 0.00758 32 [email protected] [email protected]

PI 0.35952 0.037665 0 5.45 34 23.7 0 [email protected] [email protected]

PID(Basic) 0.76098 0.0031897 -1.276 2.65 11 8.05 0 [email protected] [email protected]


Table 2. Comparative analysis of different controllers under load & no disturbance

After incorporating 2nd

order load, the design

requirement of the system is achieved in the

extended design mode which is shown in table

2.

Case3: No Load and Disturbance

In this case uncertainty in the form of step

disturbance of final value 0.5 is incorporated.




COS0301-8










mode)


COS0301-9



Controller

types

KP KI KD Rise

time

(tr

in

sec)

Settling

time (ts

in sec)

Over

shoot

(%)

Under

shoot

(%)

Gain

margin(db@

rad/sec)

Phase

margin

(deg@rad/sec)

P 0.41817 0 0 9.75 19.1 0 20 [email protected] [email protected]


PID(Basic) 0.43964 0.0019183 -1.1032 6.53 11.6 1.12 0 [email protected] [email protected]


Table 3. Comparative analysis of different controllers under no load & disturbance

Hence after incorporating disturbance, the

design requirement fulfill in Basic design

mode which is shown in table 3.

Case4: Load and Disturbance

In this case uncertainty in the form of step

disturbance of final value 0.5 and a 2nd

order

load is incorporated.



COS0301-10










COS0301-11



mode)



Controller

types

KP KI KD Rise

time

(tr

in

sec)

Settling

time (ts

in sec)

Over

shoot

(%)

Under

shoot

(%)

Gain

margin(db@

rad/sec)

Phase

margin

(deg@rad/sec)

P 0.36613 0 0 10.4 20.3 0 20 [email protected] [email protected]


PID(Basic) 0.79159 0.0033399 -1.2026 2.47 10.7 24 3 [email protected] [email protected]


Table 4. Comparative analysis of different controllers under load & disturbance

Hence after having uncertainty in the form of

both 2nd

order load and step disturbance, the

best controller performance is achieved in

extended design mode which is shown in table

4.

IV. CONCLUSION

Electric machines are used to generate

electrical power in power plants and provide

mechanical work in industries. The DC

machine is considered to be basic electric

machines. The aim of this paper is to

introduce Technicians to the modeling of

power components and to use computer

simulation as a tool for conducting transient

and control studies. Next to having an actual

system to experiment on, simulation is often

chosen by engineers to study transient and

control performance or to test conceptual

designs.

MATLAB/SIMULINK is used because of the

short learning curve that most students require

to start using it, its wide distribution, and its

general-purpose nature. This will demonstrate

the advantages of using MATLAB for

analyzing power system steady state behavior

and its capabilities for simulating transients in


COS0301-12

power systems and power electronics,

including control system dynamic behavior.

This paper has demonstrated the

implementation of auto tuning of PID

controller in MATLAB/SIMULINK, both in

basic and extended design mode. This is easy

to implement and requires a small amount of

time. The controller showed robust

performance of the system under different

cases and optimization of our system is

achieved which is less complex than other

controllers and optimization technique.

V. REFERENCES

[1] Steven T.Karris, „Introduction to Simulink

with Engineering Applications‟, Orchard

Publications,www.orchardpublications.com

[2] Tan Kiong Howe, May 2003, Thesis, B.E

(Hons.), „Evaluation of the transient

response of a DC motor using

MATLAB/SIMULINK‟, University of

Queensland.

[3] Math Works, 2001, Introduction to

MATLAB, the Math Works, Inc.

[4] O. Dwyer, .PI And PID Controller Tuning

Rules for Time Delay Process: A

Summary. Part 1: PI Controller Tuning

Rules, Proceedings of Irish Signals and

Systems Conference, June1999

[5] Raghavan S. Digital control for speed and

position of a DC motor. MS Thesis, Texas

A&M University, Kingsville, 2005.

[6] M. Chow and A. Menozzi, “on the

comparison of emerging and conventional

techniques for DC motor control,” Proc.

IECON, pp. 1008- 1013, 1992.

[7] SimPowerSystems for use with Simulink,

users guide, Math Works Inc., Natick,

MA, 2002.

[8] S. Li and R. Challoo, Restructuring

electric machinery course with an

integrative approach and computer-

assisted teaching methodology, IEEE

Trans Educ. 49 (2006), 16_28.

[9] J. J. D‟Azzo and C. H. Houpis, Linear

control system analysis and design,

McGraw-Hill, New York, 1995.

[10] S. J. Chapman, Electric machinery

fundamentals, 3rd

ed., WCB/McGraw-Hill,

New York, 1998.


COS0302-1

A SURVEY ON CURRENT CONVEYOR: NOVEL

UNIVERSAL ACTIVE BLOCK

1 Indu Prabha Singh and 2 Dr. Kalyan Singh

1Deptt. of Electronics and Comm. Engg. SITM, UNNAO-209859, India

2Dept. of Physics and Electronics Engg. , Dr. RML AVADH UNIVERSITY, FAIZABAD, India

(Email: [email protected], [email protected] )

ABSTRACT

Current conveyors (CCs) are being

increasingly employed to replace operational

amplifiers in almost all analog signal-

processing applications because their current

mode architectures are particularly suitable

for today’s low-voltage high frequency

applications. In this paper, different basic

current amplifier topologies are discussed.

With these amplifiers, more complicated

current-mode amplifiers are constructed in the

CMOS Integration technology.

Key Words: current conveyor, current

feedback amplifier, operational amplifier,

higher-bandwidth, CCI.

1. INTRODUCTION

Current conveyors have been used as a

basic building block in a variety of

electronic circuit in instrumentation and

communication systems. Today they

replace the conventional Op-amp in so

many applications such as active filters,

analog signal processing, and converters.

The current-conveyor, published in 1968

[1], represented the first building block

intended for current signal processing. In

1970 appeared the enhanced version of the

current-conveyor: the second-generation

current-conveyor CCII [2]. They used high

quality PNP - NPN transistors of a like

polarity and match each other but

difference in current gain reduced the

circuit accuracy. This is due to the base

current error. The other current conveyor

was devised in 1984 by G. Wilson [3]

where another current-mirror configuration;

known as Wilson current mirror was

employed. It consists of an operational

amplifier and external PNP transistors. A

second generation current conveyor (CCII)

was presented in 1990 using an operational

amplifier and external CMOS transistors

[4]. Both circuits were subject to the OP-

AMP performance and again to the

transistor mismatching. During that time,

research societies started to notice that the

voltage-mode operational amplifier is not

necessarily the best solution to all

analogue circuit design problems. New

research findings regarding current-mode

signal processing using current-conveyors

were presented. Furthermore, a

commercial product became available: the

current-feedback operational amplifier [5,

6]. The high slew rate and wide bandwidth

of this amplifier resulted in its popularity

in video amplifier applications.

The current conveyor is receiving

considerable attention as they offer analog

designers some significant advantages over

the conventional op-amp. These

advantages can be pointed out as follows:

Improve AC performance with better

linearity.

Wider and nearly constant bandwidth

independent of closed loop gain.

Relatively High slew rate (typically

2000V/μs).




COS0302-2

Flexibility of driving current or voltage

signal output at its two separate nodes,

hence suitable for current and voltage mode

devices.

Reduced supply voltage of integrated

circuits.

Accurate port transfer ratios equal to

unity hence employed in low sensitivity

design.

Requirement of smaller number of

passive components to perform a specific

function.

In 1988 the principle of a MOS current

copier was presented [7], which enabled

analogue circuit designers to design

different Current Conveyors using only

MOS-transistors. Therefore, apart from

above advantages following are the driving

force behind the development of MOS

Current Conveyor:

Analog VLSI addresses almost all real

world problems and finds exciting new

information processing applications in

variety of areas such as integrated

sensors, image processing, speech

recognition, hand writing recognition

etc [5]. The need for low-voltage low-

power circuits is immense in portable

electronic equipments like laptop

computers, pace makers, cell phones etc.

Voltage Mode Circuits are rarely used

in low-voltage circuits as the minimum

bias voltages depend on the threshold

voltages of the MOSFETs. However, in

current mode circuits (CMCs), the

currents decide the circuit operation and

enable the design of the systems that

can operate over wide dynamic range.

MOS-transistors in particular are more

suitable for processing currents rather

than voltages because the output signal

is current both in common-source and

common-gate amplifier configurations.

Common-drain amplifier configuration

is almost useless at low supply voltages

because of the bulk-effect present in

typical CMOS-processes.

MOS current-mirrors are more

accurate and less sensitive to process

variation. Therefore, MOS-transistor

circuits should be simplified by using

current signals in preference to voltage

signals. For this reason, integrated

current-mode system realizations are

closer to the transistor level than the

conventional voltage-mode realizations.

When signals are widely distributed as

voltages, the parasitic capacitances are

charged and discharged with the full

voltage swing, which limits the speed

and increases the power consumption

of voltage-mode circuits. Current-

mode circuits cannot avoid nodes with

high voltage swing either but these are

usually local nodes with less parasitic

capacitances. Therefore, it is possible

to reach higher speed and lower

dynamic power consumption with

current-mode circuit techniques.

Current-mode interconnection circuits

in particular show promising

performance

2. BASIC CURRENT CONVEYOR

A CC is a three or more port (X, Y, Z)

network. Whose input-output relationship

is given by:

(1)

Where A, B, C assume a value either 1, 0

or - 1 and RX is the intrinsic resistance

offered by the port X to the input currents.

For an ideal CC VX = VY and the input

resistance (RX) at port X is zero (equation

(1)). But in practical CCs, RX is a nonzero

positive value.

The commonly used block representation

of a CC is shown in Figure 1, where X and

Y are the input terminals and Z is the

output terminal.


COS0302-3

Figure1: GeneralCurrentConveyor Symbol

3. FIRST GENERATION CURRENT-

CONVEYOR (CCI)

The first generation current conveyor CCI

forces both the currents and the voltages in

ports X and Y to be equal and a replica of

the currents is mirrored (or conveyed) to

the output port Z. Port Y is used as input

for voltage signals and it should not load

the input voltage source by drawing

current. But, in some applications, it is

desirable to draw currents from the input

voltage source. When A = 1, port Y draws

a current equal to the current injected at

port X and the configuration is termed as

CCI.

Figure 2 presents a simple MOS

implementation of the first generation

current-conveyor CCI. In this circuit, the

NMOS transistors M1 and M2 form a

current mirror that forces the drain currents

of the PMOS transistors M3 and M4 to be

equal and hence the voltages at the

terminals X and Y are forced to be

identical.

Figure2: simple MOS implementation of the

first generation current-conveyor CCI

Because of this low impedance at the input

terminal CCI circuit can be used as an

accurate current amplifier. In addition, the

DC-voltage level at the current input X can

be easily set to a desired value by the

voltage at the Y-terminal and input

voltage-to-current conversion is easier .It

can also be used as a negative impedance

converter (NIC) [12], if the Y-terminal is

terminated with a grounded resistance R.

The impedance at the terminal X equals

4.SECOND GENERATION CURRENT-

CONVEYOR ( CCII)

In many applications, only one of the

virtual grounds in terminals X and Y of the

first generation current-conveyor is used

and the unused terminal must be grounded

or otherwise connected to a suitable

potential. This grounding must be done

carefully since a poorly grounded input

terminal may cause unwanted negative

impedance at the other input terminal.

Moreover, for many applications a high

impedance input terminal is preferable. For

these reasons, the second generation

current-conveyor was developed. It has

one high and one low impedance input

rather than the two low impedance inputs

of the CCI [2, 18-20].

Figure3: The principle of the second

generation current-conveyors.

(a) The positive conveyor CCII+, iz = ix.

(b) The negative conveyor CCII-, iz =- ix.


COS0302-4

This current-conveyor differs from the first

generation conveyor in that the terminal Y

is a high impedance port, i.e. there is no

current flowing into Y(A = 0). The Y-

terminal of the second generation current-

conveyor is a voltage input and the Z-

terminal is a current output, the X-terminal

can be used both as a voltage output and as

a current input. Therefore, this conveyor

can easily be used to process both current

and voltage signals unlike the first

generation current-conveyor or the

operational amplifier.

A further enhancement to the second

generation current-conveyor is that there

are two types of conveyors: in the positive

current-conveyor CCII+, the currents ix

and iz have the same direction as in a

current-mirror and in the negative current-

conveyor CCII-the currents ix and iz have

opposite direction as in a current buffer.

The second-generation current-conveyor is

in principle a voltage-follower with a

voltage input, Y, and a voltage output, X,

and a current-follower (or current-inverter)

with a current input X and a current output

Z connected together (Figure 3). The

negative second-generation current-

conveyor CCII- can also be considered an

idealised MOS-transistor, where the

currents iy =ig =0 and iz =id = - ix = - is and

the voltages vx = vs = vy = vg. An ideal

MOS transistor is one that has a zero

threshold voltage Vt and zero channel

length modulation parameter ϒ and

operates in the saturation region regardless

of the drain-source voltage (positive or

negative).

5. THIRD GENERATION CURRENT-

CONVEYOR (CCIII)

Current-conveyor III was proposed in

1995 [9]. The operation of the third

generation current-conveyor CCIII is

similar to that of the first order current-

conveyor CCI, with the exception that the

currents in ports X and Y flow in opposite

directions (A= -1). As the input current

flows into the Y-terminal and out from the

X-terminal, the CCIII has high input impedance with common-mode current signals, i.e. identical currents are fed both to Y- and X-terminals. Therefore common-mode currents can push the input terminals out from the proper operation range. Therefore this conveyor is used as current probing.

6. CURRENT-FEEDBACK OPERAT-

IONAL AMPLIFIER

The current-feedback operational amplifier

is positive second generation current-

conveyor CCII+ with an additional voltage

buffer at the conveyor current output (5, 7,

8). The non-inverting port (Y) exhibits high

impedance to voltage signals where as the

inverting port (X) present low impedance to

the input current signals. The current at the

inverting input (X) of the current-feedback

operational amplifier is transferred to the

high impedance current-conveyor output Z,

causing a large change in output voltage.

The current-feedback operational amplifier

has a transresistance equal to the

impedance level at the conveyor Z-output.

Therefore, in the literature, the current-

feedback operational amplifier is also

referred to as a transimpedance amplifier.

The most commercial current-feedback

operational amplifier is AD844 [11],

where the user has access to the high

impedance node TZ. This amplifier can

also be utilised as a second generation

current-conveyor and current to-voltage

converter. The applications and advantages

Figure 4: The operating principle of the

current-feedback operational amplifier.


COS0302-5

in realizing active filter transfer function

using CFAs have received great attention

because the amplifier enjoys the feature of

constant feedback independent of closed

loop gain and high slew rate besides

having low output impedance. Thus it is

advantageous to use CFA as a basic

building block in the accomplishment of

various analog signal-processing tasks.

7. OPERATIONAL FLOATING

CONVEYOR The operational floating conveyor (Figure 5) is a current-mode building block that combines the transmission properties of a current-conveyor and a current-feedback operational amplifier, and has an additional output current sensing capability [13]. The matrix representation of the operational floating conveyor is

(2)

Where Zt is the transimpedance of the

internal current-feedback operational

amplifier.

If a current-conveyor is a voltage-follower

with an additional output current-sensing

circuit, the operational floating conveyor is

a current-feedback operational amplifier

with a similar output current-sensing

circuit. Alternatively this conveyor can be

constructed of two cascaded current-

Figure 5: The operational floating conveyor

constructed of two second generation current-

conveyors.

conveyors. With this circuit, it is possible

to realise all four types of amplifiers:

voltage, current, transconductance, and

transimpedance amplifiers, as presented in

Figure 6. The voltage amplifier in Figure 6

operates identically to the current-feedback

operational amplifier realization of the

noninverting voltage amplifier.The four

amplifier types can also be realised with

second generation current-conveyors as

open loop amplifiers. However, when

operational floating conveyor realisations

are used, the amplifier gain is less sensitive

to finite X-terminal impedance. Since the

feedback reduces impedance levels at both

X- and W-terminals, the band- widths of

the amplifiers are less sensitive to parasitic

capacitances. Furthermore, the feedback

also reduces distortion at low frequencies

but still the current signal path from W- to

Z-terminal remain outside the feedback

loop and thus the nonlinearity remains

unchanged in that part.

8. COMPOSITE CONVEYORS

The operational floating conveyor can be

also configured to form a high

performance second generation current-

conveyor as presented in Figure 7a. This is


COS0302-6

Figure 6: Basic amplifier types realised with

operational floating conveyor.

(a) Voltage amplifier (b) Current amplifier

(c) Transconductance (d) Transresistance

a useful technique for designing CMOS

current-conveyors: with two poorly

operating simple CMOS positive second

generation conveyors, one positive

conveyor with enhanced X-terminal

impedance Zx can be constructed. In the

case of simple CMOS conveyors even the

resistor RF can generally be omitted as the

X-terminal is high enough to prevent any

stability and settling problems. There is an

alternative way to construct a composite

conveyor which lowers the X-terminal

impedance. This composite CCII- is

presented in Figure 7 b [14]. In this

composite conveyor, the lower conveyor

CC2 works as a negative impedance

conveyor and consequently the X-terminal

impedance of the composite CCII- is

Figure 7: Different composite conveyors.

(a) A composite CCII+ with enhanced Zx

resembling an operational floating

conveyor.

(b) A composite CCII- with a different

technique to lower Zx.

(c) A composite CCII+ with enhanced Yx.

9.FULLY DIFFERENTIAL CURRENT

CONVEYOR ( FDCCII) Recently, a new active element called the

FDCCII has been proposed [15] to

improve the dynamic range in mixed-

mode applications where fully differential

signal processing is required. The matrix

input–output relationship of the eight-

terminal FDCCII is:

(3)

10. OPERATIONAL FLOATING

CURRENT CONVEYOR (OFCC)


COS0302-7

The OFCC is a five-port network,

comprised of two inputs and three output

ports, as shown in matrix representation 4.

In this representation, the port labelled X

represents a low-impedance current input,

port Y is a high-impedance input voltage,

W is a low-impedance output voltage, and

Z+, and Z- are the high-impedance current

outputs with opposite polarities. The

OFCC operates where the input current at

port X is multiplied by the open loop

transimpedance gain to produce an output

voltage at port W. The input voltage at

port Y appears at port X and, thus, a

voltage tracking property exists at the

input port. Output current flowing at port

W is conveyed in phase to port Z+ and out

of phase with that flowing into port Z-, so

in this case, a current tracking action exists

at the output port. Thus, the transmission

properties of the ideal OFCC can be

conveniently described as

(4)

where iy and vy are the inward current and

voltage at the Y port, respectively, as

shown in Fig. 4. ix and vx are the input

current and voltage at the X port,

respectively. iw and vw are the output

current and voltage at W port, respectively.

iZ and vZ and are the output current and

voltage at Z+ port, respectively. Similarly,

iZ- and vZ- and are the output current and

voltage at the Z- port, respectively.

11. CONVERSION OF VOLTAGE-

MODE CIRCUIT TO CURRENT –

MODE: ADJOINT PRINCIPLE

As a wide range of voltage-mode analog

circuits already exist, a straight forward

method of converting these voltage-mode

circuits to current-mode circuits would be

very useful. In such a method a circuit

using voltage amplifiers and passive

components is converted into one that

contains current amplifiers and passive

components. An ideal voltage amplifiers

has infinite input impedance and zero

output impedance, while an ideal current

amplifier has zero input impedance and

infinite output impedance. Consequently,

direct replacement of a voltage amplifier

with a current amplifier will lead to

different circuit behaviour.

A voltage-mode circuit can be converted

into a current-mode circuit by constructing

an interreciprocal network by using the

adjoint principle [1, 17].According to this

principle, a network is replaced with an

adjoint network Na, the voltage excitation

is interchanged to a current response, and

the voltage response is interchanged to a

current excitation, as demonstrated in

Figure 8.Thus, the resulting transfer

functions of these two networks N and Na

are identical:

(5)

Figure 8: Interreciprocal networks N and Na.

The networks N and Na are thus said to be

inter-reciprocal to one another. When the

networks N and Na are identical, for

example in the case of passive networks,

the networks are said to be reciprocal. In

order to maintain identical transfer

functions for both the original network N

and the adjoint network Na the impedance

levels in the corresponding nodes of both

networks should be identical. Therefore,

the signal flow is reversed in the adjoint

network and a voltage source is converted

to a current sensing element as they both


COS0302-8

behave as short circuits. Similarly, a

voltage sensing element is converted to a

current source. A list of circuit elements

and their adjoint elements are presented in

the Table 1.

Table 1: Some circuit elements with their

corresponding adjoint elements

In addition, controlled sources can be

converted with the same principles: the

signal flow is reversed and the impedance

level is kept the same. In this way, a

voltage amplifier is converted to a current

amplifier and a current amplifier is

converted to a voltage amplifier,

respectively. However, since

transresistance and transconductance

amplifiers are inter-reciprocal, networks

containing only transresistance or

transconductance amplifiers and passive

elements differ only in signal direction and

type.

The adjoint principle can also be applied to

transistor level circuits. In this case, a

bipolar transistor in a common-emitter

amplifier configuration is inter-reciprocal

to itself and the common-collector

amplifier configuration has the common-

base configuration as its adjoint.

Converting a voltage-mode bipolar

transistor circuit to a current-mode MOS-

transistor circuit could be beneficial as it

minimises the use of source-follower

stages which have poor low-voltage

performance due to the bulk effect. Bipolar

transistor circuits are conventionally

constructed of common-emitter and

common-collector amplifier stages and the

resulting MOS-transistor adjoint circuit is

constructed of common-source and

common-gate amplifier stages.

Figure 9: Sallen- Key active biquad Filter

using Op-amp

Figure10: Sallen-Key active biquad Filter

using Current conveyor II

Using the adjoint principle the low-pass

Sallen-Key circuit can be replaced with a

current conveyor based circuit. The


COS0302-9

transfer function is the same for both of the

circuits

(a)

(b) Figure 11: Instrumentation amplifiers (a) using

Op-Amp (b) using CCII+

As shown in figure11, Instrumentation

amplifiers implemented with three op-

amps. It requires several matched resistors

to guarantee high CMRR because of the

limited gain-bandwidth product of the

high-gain amplifiers the bandwidth of the

CMRR is limited. A differential amplifier

with high CMRR can be also realised with

two current conveyor and two resistors

without any matching components. The

bandwidth of the current conveyor based

amplifier is large with high voltage gains

as current conveyors operate in open-loop

without the gain-bandwidth product

limitation.

12. CONCLUSION

In this paper, we have presented

classification and some advanced

applications of CCs. The concept of

modularity has been introduced in analog

circuit design through reconfiguring a

current conveyor as CFAs and OFCs.

Some of the performance parameters are

bandwidth, power dissipation etc. We have

seen that these CCs can perform better

than operational amplifier based circuits in

almost all signal-processing applications.

These circuits are now used in custom-

built analog ICs. It is possible to explore

new type of devices and their applications.

REFERENCES

[1] K. Smith, A. Sedra, “The current-conveyor-a new circuit building block,” IEEE Proc., vol. 56, pp. 1368-69, 1968. [2] A. Sedra, K. Smith, “A second-generation current-conveyor and its applications,” IEEE Trans., vol. CT-17, pp. 132-134, 1970. [3] D. Frey, “Log-domain filtering: an approach to current-mode filtering,” IEE Pro-ceedings G, vol. 140, pp. 406-416, Dec. 1993. [4] J. Hughes, N. Bird, I. Macbeth, “Switched currents - a new technique for analog sampled-data signal processing,” in Proc. IEEE Int. Symposium on Circuits and Systems (ISCAS’89), Portland, USA, 1989, pp. 1584-1587. [5] C. Toumazou, F. J. Lidgey, D. G. Haigh (ed.), Analogue IC design: the current-mode approach, London, Peter Peregnirus Ltd, 1990, 646 p. [6] Comlinear Corporation, A new approach to op-amp design, Application Note 300-1, March 1985. [7] S. Daubert, D. Vallancourt, Y. Tsividis, “Current copier cells,” Electronics Let-ters, vol. 24, pp. 1560-1561, Dec. 1988. [8] F. J. Lidgey, K. Hayatleh, “Current-feedback operational amplifiers and applica-tions,”Electronics &


COS0302-10

Communications Engineering Journal, vol. 9, pp. 176-182, Aug. 1997. [9] A. Fabre, “Third-generation current conveyor: a new helpful active element,” Electronics Letters, vol. 31, pp. 338-339, March 1995. [10] K. Manetakis, C. Toumazou, “A new high-frequency very low output impedance CMOS buffer,” Proc. IEEE Int. Symposium on Circuits and Systems (ISCAS-96), Atlanta, 1996 pp. 485-488. [11] Soliman, A. M. (1998) A new filter

configuration using current feedback

op-amp. Micoelectronics Journal, 29, 409-419.

[12] R. Brennan, T. Viswanathan, J. Hanson, “The CMOS negative impedance con-verter,” IEEE J. Solid State Circuits, vol. 23, pp. 1272-1275, Oct. 1988. [13] C. Toumazou, A. Payne, F. Lidgey, “Operational floating conveyor,” Electronic Letters, vol. 27, pp. 651-652, April 1991. [14] A. Fabre, H. Barthelemy, “Composite second-generation current conveyor with reduced parasitic resistance,” Electronic Letters, vol. 30, pp. 377-378, March1994. [15] 1 El-Adawy, A.A., Soli an, A.M., and

Elwan, H.O.: „A novel fully differential

current conveyor and applications for analog

VLSI‟, IEEE Trans Circuits Syst. II, Analog

Digit. Signal Process, 2000, 47,(4),pp. 306–

[18] Sanchez-Sinencio, E., and Andreou,

A. G. ed. (1999). “Low voltage/low power

integrated circuits and systems”, IEEE

Press.

[19] Barthelemy, H., and Fabre, A. (2002).

“A second generation current-controlled

conveyor with negative intrinsic

resistance”, IEEE Trans. Circuits and

Systems-I, 49(1), 63–65.

[20] Fabre, A., and Alami, M. (1997). “A

precise macromodel for second generation

current-conveyors”, IEEE Trans. Circuits

and Systems-I, 44(7), 639–642.


COS0401-1

A COMPARITIVE STUDY OF GENETIC ALGORITHM

AND THE PARTICLE SWARM OPTIMIZATION

Sapna Katiyar, Deepika pandey, Sakshi Chhabra, Vaishali Gupta,

A.B.E.S. Institute of Technology, NH-24, Vijay Nagar, Ghaziabad (UP) 201009,

(e-mail: [email protected], [email protected], [email protected],

[email protected])

Corresponding Author: [email protected]

ABSTRACT

Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are

inspired by the swarming or collaborative behavior of biological populations.PSO is similar to the Genetic

Algorithm (GA) as these two evolutionary heuristics are population-based search methods. In other words,

PSO and the GA move from a set of points (population) to another set of points in a single iteration with

likely improvement using a combination of deterministic and probabilistic rules. The GA and its many

versions have been popular in the academy and the industry mainly because of its intuitiveness, ease of

implementation, and the ability to effectively solve highly nonlinear, mixed integer optimization problems

that are typical of complex engineering systems. The drawback of the GA is its expensive computational cost.

This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal

solution) as the GA but with significantly better computational efficiency (less function evaluations) by

implementing statistical analysis and formal hypothesis testing. The major objective of this paper is to

compare the computational effectiveness and efficiency of the GA and PSO using a formal hypothesis testing

approach.

Index Terms - Genetic algorithm, Candid solution, metaheuristics, Numerical optimization, stochastic, swarm,

variants, hybrid PSO, PSO parameters

__________________________________________________________________________________________

I. BACKGROUND

The Genetic Algorithm (GA) was introduced in the

mid 1970s by John Holland and his colleagues and

students at the University of Michigan. The GA is

inspired by the principles of genetics and evolution,

and mimics the reproduction behavior observed in

biological populations. The GA employs the principal

of “survival of the fittest” in its search process to

select and generate individuals (design solutions) that

are adapted to their environment (design

objectives/constraints). Therefore, over a number of

generations (iterations), desirable traits (design

characteristics) will evolve and remain in the genome

composition of the population (set of design solutions

Generated each iteration) over traits with weaker

undesirable characteristics. The GA is applied to

solve complex design optimization problems because

it can handle both discrete and continuous variables

and nonlinear objective and constrain functions

without requiring gradient information.

Particle Swarm Optimization (PSO) was invented

by Kennedy and Eberhart in the mid 1990s while

attempting to simulate the choreographed, graceful

motion of swarms of birds as part of a socio cognitive

study investigating the notion of “collective

intelligence” in biological populations. In PSO, a set

of randomly generated solutions (initial swarm)

propagates in the design space towards the optimal

solution over a number of iterations (moves) based

on large amount of information about the design

space that is assimilated and shared by all members

Of the swarm. PSO is inspired by the ability of flocks

of birds, schools of fish, and herds of animals to

adapt to their environment, find rich sources of food,

and avoid predators by implementing an “information

sharing” approaches, hence, developing an

evolutionary advantage.

II. GENETIC ALGORITHM


COS0401-2

Start

Create randomly selected

initial population

Fitness

Evaluation

Stop

Reproduction

Mutation

Discard

Fit Chromosome?

Optimal

Solution?

No

No

Yes

Yes

Fig 1: Flow Chart of the Genetic Algorithm

In a genetic algorithm, a population of strings

(called chromosomes or the genotype of the genome),

which encode candidate solutions (called phenotypes)

to an optimization problem, evolves toward better

solutions. Solutions are represented in binary as

strings of 0s and 1s, but other encodings are also

possible. The evolution usually starts from a

population of randomly generated individuals and

happens in generations. In each generation, the

fitness of every individual in the population is

evaluated, multiple individuals are stochastically

selected from the current population (based on their

fitness), and modified (recombined and possibly

randomly mutated) to form a new population. The

new population is then used in the next iteration of

the algorithm. The algorithm terminates when either

a maximum number of generations has been

produced, or a satisfactory fitness level has been

reached for the population. If the algorithm has

terminated due to a maximum number of generations,

a satisfactory solution may or may not have been

reached.

A typical genetic algorithm requires:

A genetic representation of the

solution domain

A fitness function to evaluate the solution

domain.

A standard representation of the solution is as

an array of bits. The main property that makes these

genetic representations convenient is that their parts

are easily aligned due to their fixed size, which

facilitates simple crossover operations. Variable

length representations may also be used, but

crossover implementation is more complex in this

case. Tree-like representations are explored in genetic

programming and graph-form representations are

explored in evolutionary programming.

A. OBJECTIVE FUNCTION OF GENETIC

ALGORITHM

The fitness function is defined over the

genetic representation and measures the quality of the

represented solution. The fitness function is always

problem dependent. For instance, in the knapsack

problem one wants to maximize the total value of

objects that can be put in a knapsack of some fixed

capacity.

A representation of a solution might be an

array of bits, where each bit represents a different

object, and the value of the bit (0 or 1) represents

whether or not the object is in the knapsack. Not

every such representation is valid, as the size of

objects may exceed the capacity of the knapsack.

The fitness of the solution is the sum of

values of all objects in the knapsack if the

representation is valid or 0 otherwise. In some

problems, it is hard or even impossible to define the

fitness expression; in these cases, interactive genetic

algorithms are used.

Once we have the genetic representation and

the fitness function defined, GA proceeds to initialize

a population of solutions randomly, and then improve

it through repetitive application of mutation,

crossover, and inversion and selection operators.

B. IMPLEMENTATION ALGORITHM

The genetic algorithm uses the chromosomes fitness

value to create a new population consisting of the

fittest members. The flow chart of the GA is given

here in Fig 1.


COS0401-3

C. APPLICATIONS

Genetic algorithms find application in bioinformatics,

phylogenetics, computational science, engineering,

economics, chemistry, manufacturing, mathematics,

physics and other fields.

Learning Robot behavior using Genetic Algorithms:

Robot has become such a prominent tools that it has

increasingly taken a more important role in many

different industries. As such, it has to operate with

great efficiency and accuracy. This may not sound

very difficult if the environment in which the robot

operates remain unchanged, since the behaviors of

the robot could be pre-programmed. However, if the

environment is ever-changing, it gets extremely

difficult, if not impossible, for programmers to figure

out every possible behaviors of the robot. Applying

robot in a changing environment is not only

inevitable in modern technology, but is also

becoming more frequent. This has obviously led to

the development of a learning robot.

III.PARTICLE SWARM OPTIMIZATION

The PSO was first designed to simulate birds seeking

food which is defined as a “cornfield vector.” The

bird would find food through social cooperation with

other birds around it (within its neighborhood). It was

then expanded to multidimensional search.

Particle swarm optimization (PSO) is a

computational method that optimizes a problem

by iteratively trying to improve a candidate solution

with regard to a given measure of quality. Such

methods are commonly known as metaheuristics as

they make few or no assumptions about the problem

being optimized and can search very large spaces of

candidate solutions. However, metaheuristics such as

PSO do not guarantee an optimal solution is ever

found.

PSO does not use the gradient of the problem being

optimized, which means PSO does not require for the

optimization problem to be differentiable as is

required by classic optimization methods such

as gradient descent and quasi-newton methods. PSO

can therefore also be used on optimization problems

that are partially irregular, noisy, change over time,

etc.

PSO optimizes a problem by having a population

of candidate solutions, here dubbed particles, and

moving these particles around in the search-space

according to simple mathematical formulae. The

movements of the particles are guided by the best

found positions in the search-space which are

updated as better positions are found by the particles.

PSO algorithm works by having a population (called

a swarm) of candidate solutions (called particles).

These particles are moved around in the search-space

according to a few simple formulae. The movements

of the particles are guided by their own best known

position in the search-space as well as the entire

swarm's best known position. When improved

positions are being discovered these will then it will

guide the movements of the swarm. The process is

repeated and satisfactory solution will be discovered.

A.PSO VARIANTS

Various variants of a basic PSO algorithm are

possible. New and some more sophisticated PSO

variants are continually being introduced in an

attempt to improve optimization performance. There

is a trend in that research; one can make a hybrid

optimization method using PSO combined with other

optimization techniques.

Discrete PSO

Constriction Coefficient

Bare-bones PSO

Fully informed PSO

B. APPLICATIONS

The first practical application of PSO was in the field

of neural network training and was reported together

with the algorithm itself (Kennedy and Eberhart

1995). Many more areas of application have been

explored ever since, including telecommunications,

control, data mining, design, combinatorial

optimization, power systems, signal processing, and

many others. PSO algorithms have been developed to

solve:

Constrained optimization problems

Min-max problems

Multi objective optimization problems

Dynamic tracking

C. IMPLEMENTATION ALGORITHM

The PSO algorithm is simple in concept, easy to

implement and computational efficient. Original PSO

was implemented in a synchronous manner (Fig 2)

but improved convergence rate is achieved by

asynchronous PSO algorithm Fig 2.


COS0401-4

Fig 2: Synchronous PSO algorithm (Parallel

Processing)

IV. GENETIC ALGORITHM VERSUS

PARTICLE SWARM OPTIMIZATION

1) GA is inherently discrete, i.e. it encodes the

design variables into bits of 0’s and 1’s,

therefore it easily handles discrete design

variables, and PSO is inherently continuous

and must be modified to handle discrete

design variables.

2) Unlike the GA with its binary encoding, in

PSO, the design variables can take any

values, even outside their side constraints,

based on their current position in the design

space and the calculated velocity vector.

V. CONCLUSIONS

Particle Swarm Optimization (PSO) is a relatively

recent heuristic search method that is based on the

idea of collaborative behavior and swarming in

biological populations. PSO is similar to the Genetic

Algorithm (GA) in the sense that they are both

population-based search approaches and that they

both depend on information sharing among their

population members to enhance their search

processes using a combination of deterministic and

probabilistic rules. Conversely, the GA is a well

established algorithm with many versions and many

applications.

GA is very helpful when the developer does not have

precise domain expertise, because GAs possesses the

ability to explore and learn from their domain. PSO

can be applied to multi-objective problems, in which

the fitness comparison takes pareto dominance into

account when moving the PSO particles and non-

dominated solutions are stored so as to approximate

the pareto front.

The objective of this research paper is to test

the hypothesis that states that although PSO and the

GA on average yield the same effectiveness (solution

quality), PSO is more computationally efficient (uses

less number of function evaluations) than the GA.

REFERENCES

[1] Goldberg, D.E. (1989). Genetic Algorithms in

Search, Optimization and Machine Learning,

Addison-Wesley.

[2] Kennedy, J. and Eberhart, R., “Particle Swarm

Optimization,” Proceedings of the IEEE

International Conference on Neural Networks,

Perth, Australia 1995, pp. 1942-1945.

[3] Kennedy, J. and Eberhart, R., Swarm Intelligence,

Academic Press, 1st ed., San Diego, CA, 2001.

[4] Yuhui Shi. Particle Swarm Optimization.

Electronic Data Systems, Inc. IEEE Neural Network

Society.

[5] R. Poli. Analysis of the publications on the

applications of particle swarm optimization. Journal

of Artificial Evolution and Applications, Article ID

685175, 10 pages, 2008.

[6] R. Poli, J. Kennedy, and T. Blackwell. Particle

swarm optimization. An overview. Swarm

Intelligence, 1(1):33-57, 2007.

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