35362429 Speed Estimation

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SPEED ESTIMATION TECHNIQUES FOR

INDUCTION MOTOR USING DIGITAL SIGNAL

PROCESSING

SOLLY ARYZA

Thesis submitted in fulfillment of the requirements

For the award of the degree of

Master of Engineering (Electrical)

Faculty Kejuruteraan Electrical Engineering

UNIVERSITI MALAYSIA PAHANG

2011


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ABSTRACT

Speed estimation is one of the methods of speed sensor-less control for three

phase induction motors. With the advancement of the power electronics switching

devices and digital technologies, the developments of speed estimation methods have

been intensively implemented from many researchers. Thus, this field of research has

become more interested to investigate. Speed sensor-less control techniques can make

the hardware simple and improve the reliability of the motor without the introducing the

feedback sensor and it becomes more important in the modern AC servo drive. It is one

of the attracting research directions in the high-precision servo control field because of

its robust characteristics, simple realization and excellent dynamic response. Several

common rotor speed estimation was introduced in the thesis. The model must accurately

represent both the electrical and electromagnetic interactions within the machine and

associated mechanical systems. In this Thesis, the neural networks controller for speed

estimation has been developed approach to induction motor that has been implemented

in digital signal processing controller (DSP) and gave the control signal to IGBT for run

three phase inductions motor. Analysis of speed estimation nonlinear characteristics is

carried out and makes a comparison with traditional linear method speed sensor less

method. First, the simulation of the proposed control system is performed by using the

MATLAB software and then the real time implementation is performed by using the

MATLAB and the hardware. According to the mathematical model of the induction

motor, the simulation of model and hardware implementation of speed sensor-less

induction motor had been successfully implemented. The design and implementation of

the speed estimation system for three-phase induction motor and the experimental

research is presented in this Thesis. Finally, this Thesis shows the implementation of the

speed estimation using DSP controller and the design of hardware and software for

speed sensorless of induction motor. The experiment is completed at different speed and

experiment results show that artificial neural network controller obtained a good

response when compared to conventional methods.


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ABSTRAK

Anggaran kelajuan adalah salah satu kaedah kawalan kelajuan sensor-kurang

selama tiga fasa motor aruhan. Dengan kemajuan elektronik kuasa menukar peranti dan

teknologi digital, perkembangan kaedah anggaran kelajuan telah dilaksanakan dengan

intensif daripada ramai penyelidik. Oleh itu, bidang penyelidikan ini telah menjadi lebih

berminat untuk menyiasat. Teknik sensor-kurang kawalan kelajuan boleh membuat

mudah perkakasan dan meningkatkan kebolehpercayaan motor tanpa sensor maklum

balas memperkenalkan dan ia menjadi lebih penting dalam pemacu servo moden yang

AC. Ia merupakan salah satu arah penyelidikan yang menarik dalam bidang kawalan

servo kepersisan tinggi kerana ciri-ciri yang teguh, kesedaran mudah dan gerak balas

dinamik yang sangat baik. Beberapa kelajuan biasa anggaran pemutar telah

diperkenalkan dalam tesis. Model harus secara tepat mewakili kedua-dua interaksi

elektrik dan elektromagnet dalam mesin dan sistem mekanikal yang berkaitan. Tesis ini,

pengawal rangkaian neural untuk anggaran kelajuan telah dibangunkan pendekatan

untuk motor aruhan yang telah dilaksanakan dalam pemprosesan isyarat digit pengawal

(DSP) dan memberi isyarat kawalan ke IGBT untuk jangka masa tiga fasa motor

induksi. Analisis ciri-ciri tak linear anggaran kelajuan dijalankan dan membuat

perbandingan dengan kelajuan tradisional kaedah linear kaedah sensor yang kurang.

Pertama, simulasi sistem kawalan yang dicadangkan dijalankan dengan menggunakan

perisian MATLAB dan pelaksanaan masa nyata dilakukan dengan menggunakan

MATLAB dan perkakasan. Menurut model motor aruhan matematik, simulasi model

dan pelaksanaan perkakasan kelajuan sensor-kurang motor aruhan telah berjaya

dilaksanakan. Reka bentuk dan pelaksanaan sistem anggaran kelajuan motor aruhan tiga

fasa dan penyelidikan eksperimen dibentangkan di dalam tesis ini. Akhirnya, Tesis ini

menunjukkan pelaksanaan anggaran kelajuan menggunakan DSP pengawal dan reka

bentuk perkakasan dan perisian untuk sensorless kelajuan motor aruhan. Eksperimen itu

selesai pada kelajuan yang berbeza dan hasil eksperimen menunjukkan bahawa

pengawal rangkaian neural tiruan mendapat sambutan yang baik berbanding dengan

kaedah konvensional.


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TABLE OF CONTENTS

SUPERVISORS DECLARATION

STUDENTS DECLARATION

DEDICATION

ACKNOWLEDGEMENTS

ABSTRACT

ABSTRAK

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

LIST OF SYMBOLS AND ABREVIATION

CHAPTER 1 INTRODUCTION

1.1 Background of Research 1

1.2 State Estimation Sensor less Electric Drives 2

1.2.1 Flux Estimation for Field Orientation 4

1.2.2 Flux Observers From State Space Control Theory 4

1.3 Problem Statements 5

1.4 Research Objectives 5

1.5 Scope of Thesis Work 6

1.6 Contribution 6

1.7 Thesis Outline 6


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CHAPTER 2 LITERATURE REVIEW

2.1 Induction Motor 8

2.2.1 Stator 10

2.2.2 Squirel Cage Rotor 11

2.2 Frequency Controller 11

2.3 Space Vector Pwm Control 12

2.4 Adaptive Pid Regulator 16

2.5 Artifial Neural Network 18

2.6 Digital Signal Processing Implementation Of Speed Sensorless 23

FOC

2.6.1 Hardware Implementation of FOC 24

2.6.2 DSP Software Implementation of FOC 25

2.7 Summary 25

CHAPTER 3 METHODOLOGY

3.1 Introduction 26

3.2 Speed Estimation Using Direct Torque Control 26

3.3 Speed Estimation Using Vector Control 27

3.4 Speed Estimation Using State Equations 28

3.5 Speed Estimation Using Rotor Slot/ Bar Harmonics 31

3.5.1

Tapped Windings 32

3.5.2 Stator Current 33

3.5.3

High frequency Injection 33

3.6 Strategy Controller Motor 35

3.6.1 Scalar Control 38

3.6.2 Vector Control 41


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3.6.3 Direct Torque Control 43

3.6.4 Feedback Linearized Control 44

3.7 Intelligent Artificial Controller 47

3.8 Proposed Adaptive System Using Neural Networks 54

3.9 System Configuration 55

3.9.1 System Overview 55

3.9.2 Motor Controller 56

3.9.3 Synchronous Angle Calculator 57

3.9.4 Current Control of Voltage Converter 57

3.9.5 Inverter Controller 57

3.9.6 Speed Estimator 58

3.10 Voltage Model Compensated by Rotor FOC Model 59

CHAPTER 4 RESULTS AND DISCUSSION

4.1 Introduction 61

4.2 Simulation Result 61

4.3 Comparison of Induction Motor Operation 61

4.4 Hardware Experimental Setup 68

4.5 Performance 72

4.6 Result 73

4.7 Conclusion 76

CHAPTER 5 CONCLUSION AND FUTURE WORK

5.1 Conclusion 77

5.2 Suggestion Future Work 77

REFERENCE

APPENDIX


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LIST OF TABLES

Table No.

3.1

4.1

Title

Parameter of three phase induction motor

Comparison Operation of Induction Motor Used ANN and

Modified Voltage Model Flux

Page

59

66


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LIST OF FIGURES

Figure No. Title Page

1.1 Sensorless Speed Estimation Methods 4

2.1 Cross Section of An Induction machines 10

2.2 Resultant Vector Representation of The Three Phase Currents 11

2.3 Conducting Rotor Bars Without The Rotor 12

2.4 Open loop Voltage and Frequency Controller 14

2.5 Close Loop Speed Control With Volts/Hertz Control and slip

regulation

16

2.6 Current / Slip scalar Control scheme 19

2.7 Direct Torque Controlled Induction Motor Drive 20

2.8 Feed Back Linearization Control 20

2.9 Reference Frames and Representation of Stator current and Rotor

Flux as Space Vectors

21

2.10 Three Phase Voltages Sources Inverter 24

2.11 Configuration of Space Vector PWM 26

2.12 a Example of Space Vector PWM Pattern Reference Voltage and

Projections

28

2.12 b

2.13

Example of Space Vector PWM Pattern in Sector 3

Adaptive PID Control

28

30

2.14 Schemes of ANN for Controller 33

2.15 Experimental Set Up for Sensorless Control System 38

3.1 Speed Observer Using State Equations 43

3.2 a Rotor Flux Orientation Control Stator Reference Frame 49

3.2 b Rotor Flux Orientation Control In Estimated Rotor Flux

Reference Frame

49


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3.3 Typical Induction Motor Drive 55

3.4 Detailed Laboratory Set Up 56

3.5 Implementation Procedure of The Control and Estimation 57

3.6 Block Diagram of MRAS Speed Observer 57

3.7 Proposed System Configuration 60

3.8 Proposed Modified Voltage Model 64

4.1 Experiment Set Up Induction Motor Based On DSP 66

4.2 Block Diagram TMS320F28335 67

4.3 Induction Motor 3 phase 68

4.4 Experimental Set Up 68

4.5 Simulation Speed for High Voltage 704.6 Simulation for 35 V Constant Rotating Simulations 70

4.7 Simulation for Torque of speed Estimations IM with Percentage

Full Load

71

4.8 Modeling Speed Estimation Controlling Induction Motor 73

4.9 Stator Controlling Circuit 73

4.10 Estimatiors Adaptive in Controlling 74

4.11

4.12

4.19

Stator Current

Speed Result

Torque Result

74

75

75


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PWM

AC

HP

ANN

S

R1

R2

R0

L1

L2

L0

X1

X2

X0

Vi

V0

V1

Vf

Vb

V0

I

I1

I2

I3

LIST OF SYMBOL AND ABREVIATION

Pulse Width Modulation

Alternating current

HP Horse Power

Artificial Neural Network

Slip

Stator resistance in ohms

Rotor resistance referred to stator in ohms

Equivalent resistance corresponding to the iron losses in

ohmsLeakage inductance of Stator in henry

Leakage inductance of Rotor referred to stator in henry

Magnetizing inductance of the stator in henry

Leakage reactance of Stator in ohms

Leakage reactance of Rotor referred to stator in ohms

Magnetizing reactance of the stator in ohms

Input voltage in volts

Output voltage in volts

Voltage across the variable rotor resistance in volts

Output voltage due to forward field in volts

Output voltage due to backward field in volts

Output voltage

Current flowing through the stator in Amps

Iron-loss and magnetizing component of the noload current

in Amps due to forward field

Rotor current referred to the stator in Amps due to forward

fiel

Iron-loss and magnetizing component of the noload current

in Amps due to backward field


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I4

Pgf

Pgb

T

TL

Ns

J

B

P

Y

O

Vji

Wkj

B1

B2

Tr

Lr

Rr

Lm

Rs

Ls

v

Rotor current referred to the stator in Amps due to

backward field

Airgap power developed by the motor due to forward field

Airgap power developed by the motor due to backward field

Torque developed by the motor in Nm

Load Torque in Nm

Synchronous speed in rps

Moment of inertia in Kgm2

Viscous friction in Nms

Number of Poles

Angular speed in rad/secAngular displacement in radians

Output vector of the hidden layer

Output vector of the output layer

weight matrix

weight matrix

Bias vector

Bias vector

Input

Output

Secondary time constant

Secondary inductance per phase

Secondary resistance per phase

Magnetizing inductance per phase

Primary winding resistance per phase


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CHAPTER 1

INTRODUCTION

1.1 BACKGROUND OF RESEARCH

With the power electronics technology, computer technology and digital signal

processing technology, the high-performance motor frequency control system should be

widely applied. AC variable speed drive system with excellent and starting and braking

performance and efficient energy-saving effect, the use of the motor frequency control

technology, its capacity, speed and voltage levels can be high; speed control system is

small, lightweight, inertia, high reliability, less maintenance, suitable for harsh working

conditions and low cost. The frequency conversion technology, especially vector

control salient features of the system technology, so from household appliances to

sophisticated servo robots, and even aviation, military space industry, frequency control

technology nothing less.

In the high-performance induction motor system, closed-loop motor speed

control is a necessary part of the general essential, often using a photoelectric encoder

speed sensor for detecting speed and feedback speed signal. However, the speed sensorinstallation, maintenance, cost, and poor working conditions and other issues, are

shadowed ring to the induction motor speed control system simplicity and reliability,

limiting the scope of application of AC variable-speed system.

Thus, the use of detection of stator voltage, current, etc. easily measured

physical quantity's velocity estimation, namely, Speed sensor technology research has

important practicable significance and broad space for development (Bose B.K, 2007).


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Although the induction motor has been developed to a very advanced stage, but its

speed sensor fewer control systems also need further research, there is a relatively large

space for development. Speed sensor-less Application, one can complete high-

performance closed-loop control without the need for speed, on the other hand, reduces

the Install speed sensor in the system caused by increased hardware complexity and

reliability issues down. In earlier research in this area, and there are already useful

general-purpose inverter speed sensor products.

Therefore, development of independent intellectual property rights of speed

sensor less AC drive products have become when the service.

1.2 STATE ESTIMATION SENSORLESS ELECTRIC DRIVES

The speed estimation is particular interest with the induction motor drives where

the mechanical speed of the rotor is generally different with speed of the revolving

magnetic field. The advantages of speed sensor induction motor drives is reduction of

hardware complexity and cost, increase of circuit noise immunity and drive reliability,

and reduction of maintenance requirements ( lin F.J and Chiu S.L, 2007 ). Operations in

the hostile environments, mostly motor drives without speed/position sensors.

Many variable-speed electric drives used in general purpose applications ranging

from a simple servo system to the complex traction system require a capability of speed

variation with a pre-defined performance standard. In such applications, it is necessary

that the actual drive-speed measurements be available at every instant to control the

drive effectively. Therefore, many different kinds of speed sensors have been used toinclude taco generators, optical encoders, resolves, etc.

Elimination of such a requirement of having the speed sensor on the motor shaft

represents a cost advantage, and also enhances the reliability of the drive owing to the

absence of a mechanical sensor and associated cable accessories. The identification of

the rotor speed is generally based on measured terminal voltages and currents. Various

dynamic models are used to estimate the magnitude and the spatial orientation of the

magnetic flux vector and for this purpose, open loop estimators or closed-loop observers


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are used, which usually differ from respect to accuracy, robustness and sensitivity

against model guideline variations. Figure 1.1 shows the block diagram of the speed

sensor less induction motor control. Basically, there are two commonly used control

methods: the voltage-to-frequency (V/f) control and the field oriented control (FOC).

Both methods for the speed sensor less control required a speed estimation algorithm. In

the V/f control, the ratio of the stator voltage to stator frequency is kept constant using

feed forward control to maintain the magnetic flux in the motor at a desired level. This

control is simple but it only cans satisfy moderate motor dynamic requirements. On the

other hand, high motor dynamic performance can be achieved using FOC control,

which is also called the vector control.

The stator currents are injected at a well-defined phase angle with respect to the

spatial orientation of the rotating magnetic field, thus overcoming the complex dynamic

properties of the induction motor. The spatial location of the magnetic field, that is the

field angle, is difficult to measure.

There are several models and algorithms that can be used for the estimation of

the field angle, for example, the open-loop estimator such as model reference adaptive

system (MRAS), or the close loop observer such as the Kalman Filter. The induction

motor control using the field orientation usually refers to the rotor field.

Figure 1.1: Sensor-less Speed Estimation Methods

(Zhang Yan and Utkin V, 2005)


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1.2.1 Flux Estimation for Field Orientation

Achieving high-quality torque and flux control in applications requiring both

zero and very high speed operation is difficult with existing approaches to induction

machines field orientation (Fitzgerald AE, 2006). There are two basic forms of field

orientation; direct field orientation (DFO) which relies on direct measurement or

estimation of the rotor or stator flux magnitude and angle and indirect field orientation

(IFO) which uses and inherent slip relation.

1.2.2 Flux Observers from State Space Control Theory

Numerous researchers have applied conventional linear observer theory from

modern state space control to the estimation of rotor flux for DFO systems, the

induction machines electric model in state space and the stationery reference frame is:

p [ ]

or simply

px= Ax + Bu

Where:

p = differential operator

= stator current = rotor flux

= stator voltage in complex vector

1.3

PROBLEM STATEMENTS

Sensors are widely used in electric drives degrade the reliability of the system

especially in hostile environments and require special attention to electrical noise.

Moreover, it is difficult to mount sensor in certain applications besides extra expenses

involved. Therefore, a lot of researches are underway to develop accurate speed

estimation techniques. With sensor-less vector control we have decoupled control


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structure similar to that of a separately excited dc motor retaining the inherent

ruggedness of the induction motor at the same time. Speed sensor-less control

techniques first appeared in 1975 (C.Ilas 2005). Several reviews and comparison paper

is available on sensor less control techniques (B. P. Panigrahi 2005). The commonly

used method for speed estimation is model reference adaptive system (MRAS) (Zhang

Yan 2008). Further, a high performance sensor less induction motor driver (G. Bartolini

2003) requires speed estimation besides estimating machine parameters most important

of which is the rotor resistance which varies during the operation of the motor. Very

few work have been reported on simultaneous estimation of speed and rotor resistance.

(A.E. Fitzgerald 2006).

1.4 RESEARCH OBJECTIVES

The primary objective of this research as follow:

-

To estimate induction motor speed using ANN

- To design Hardware and Software of the speed and current controllers for

induction motor drive using ANN method

-

To build the real time controlling MATLAB of induction machine.

1.5 SCOPE OF THESIS

- Make a hardware prototype of the proposed speed sensorless control.

- To design a nonlinear feedback controller.

- Use artificial neural network for speed estimation control and make the

comparison with another method.

1.6 CONTRIBUTION

In this thesis, some contribution has been provided as follow:

- Develop speed estimation controller using digital signal processing (DSP).

- Develop a real time speed estimation of induction machine, which can be used for

teaching purpose.


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- Develop hardware controller for speed estimation induction motor real time design.

1.7 THESIS OUTLINE

The Thesis introduction addressed the research trends in the area of controlling

motor drives for induction motor based on DSP (Digital Signal Processing) for speed

estimation. The research motivation and objectives were then explained in detail.

Chapter two describes the all the literature review, components selection and

sizing of the motor drive subsystems for the electric power transmission path, and

highlights the issues with these subsystems that have motivated this research.

Chapter three presents some method from speed estimation, mathematical model

for sensor less control induction motor , and modeling of an advanced induction motor

A literature review on existing parameter estimation methods to improve the

performance of propulsion motor drive system is also presented.

Chapter four describes the experiment laboratory and analysis calculation of

three phase induction motor, including software-in-the-loop simulation results in

experiment method of the induction Motor drive.

Chapter five concludes and future works this thesis, and presents future research

topics related controlling Motor Drives.


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CHAPTER 2

LITERATURE REVIEW

2.1 INDUCTION MOTOR

The Torque speed characteristic of an induction motor is directly related to the

resistance and reactance of the rotor (Kubota H and Matsuse K, 2006). Hence, different

Torque- Speed characteristics may be obtained by designing rotor circuits with different

ratios of rotor resistance to rotor reactance.

In induction machines, the field circuit is on the stator. The armature circuit is

on the rotor, and the rotor poles are induced by transformer action. Both the stator poles

and the rotor poles rotate at the synchronous speed, but the rotor rotates physically at a

speed slightly less than a synchronous speed and slows down as the load torque and

power requirements increase.

A rotating magnetic field, produced by a stationary winding (called the stator),

induces an alternating, cmf and current in the rotor. The resultant interaction of the

induced rotor current with the rotating field of the stationery winding produces motortorque Figure 2.1.

The stator is identical to a stator of a synchronous machine: three phases, P

poles, sinusoidal mmf and flux distribution, and synchronous speed. In induction

motors, the stator carries the field. The rotor is much different; in induction motors, the

rotor is an iron cylinder with large embedded conductors, which are shorted to allow the

free flow of current. The stator flux induces the ac current in the each of the rotor


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conductors, and an ac voltage is induced in the rotor to drive the currents. The currents

in the conductor produce a magnetic.

Figure 2.1: Cross section of an Induction machines

Induction machine technology is a mature technology with extensive research

and development activities over past 100 years. Recent development in digital signal

processor and advanced vector control algorithm allow controlling an induction

machine like a DC machine without the maintenance requirements. Induction motors

are considered as workhorses of the industry because of their low cost, robustness and

reliability. Induction machines are used in electric and hybrid electric vehicle

applications because they are rugged, lower-cost, operate over wide speed range, and

are capable of operating at high speed.

The size of the induction machine is smaller than that of a separately excited DC

machine for similar power rating. The induction machine is the most mature technology

among the commutated fewer motor drives.

There are two types of induction machines: squirrel cage and wound rotor. In

squirrel cage machines, the rotor winding consists of short-circuited copper or

aluminium bars with ends welded to copper rings known as end rings. In wound rotor

induction machines, the rotor windings are brought to the outside with the help of slip

rings so that the rotor resistance can be varied by adding external resistance. If three-

phase voltages are applied to the stator, the stator magnetic field will cut the rotor

conductors, and will induce voltages in the rotor bars. The induced voltages will cause


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rotor currents to flow in the rotor circuit, since the rotor is short-circuited. The rotor

current will interact with the air gap field to produce torque. As a result the rotor will

start rotating in the direction of the rotating field. The difference between the rotor

speed and the stator flux synchronous speed is the slip speed by which the rotor is

slipping from the stator magnetic (Xu Z and Rahma M.F, 2007).

2.1.1. Stator

The stator is made up of this laminations of a highly permeable steel that

together yields high magnetic flux density and low core losses. The stator winding are

wound and placed into slots in the stator, 120 apart in space. These are wound to

create stator magnetic poles when current flows through them. The number of stator

poles in conjunction with the frequency of the applied three phase power determined the

speed of rotation og the stators magnetic field and thereby the speed of the induction

motor. Figure 2.2 shows a stat Ice. pictures of the flux and current direction when only

winding A of the stator is energized.

While normal operation of we-connected induction machine precludes having

the current through only one winding, this drawing illustrates the resultant flux vector.

During normal operation, three currents, oriented 120 degrees part in time, are applied

to the three windings and can be combined to form a

stator vector. The stator flux

vector

It is found to be.

=

/ Rwhere R is defined to be the magnetic circuit

reluctance.

Figure 2.2: Resultant vector representation of the three phase currents.


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It is important to note that a balanced three phase current applied to the stator

winding's results in a rotating stator current and therefore, a rotating flux vector. The

magnitude of the Stator vector is constant as it rotates within a plane, it can be

represented by two components; x and y. Analysis can be greatly simplified if we

consider that the stator current has only these two components and represented it as

Is= [

] ( 2.1 )

2.1.2. Squirel Cage Rotor.

The squirrel cage rotor is also made up of thin laminations of a highly permeable

steel that together yield high magnetic flux density and low core losses. The rotor bars,

or windings, are placed in the rotor slots and shorted together at each end as shown in

Figure 2.3. The rotor is mounted on a shaft so that it can rotate and is placed inside the

stator. The number, size, skew and depth of recess of the rotor conductors influence

motor performance.

Figure 2.3: Conducting rotor bars without the rotor.

2.2 FREQUENCY CONTROLLER

The speed of an induction motor is very near to its synchronous speed. The

difference between the two being characterized by the slip speed. If the synchronous

speed of the induction motor is changed, there is corresponding change in the speedof


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Figure 2.4: Three Phase Voltages Sources Inverter

The six switches Q1 to Q6 is insulated gate bipolar transistors IGBT). The ON-OFF

sequence of these switches follows the conditions below:

- Three of the switches must be always ON and three are always OFF.

- The upper and the lower switches of the same leg are driven with two

complementary pulsed signals with a dead band betwee n the two signals to

avoid short circuit.

- The induction motor is supplied with the required three phase voltages for the

designed operating conditions using PWM technique. In this research paper, the

space vector PWM method is used to generate the gating signals for the switches

in the VSI inverter that drives the induction motor with high performance in

terms of fast response to changes of loads and speed commands.

The relationship between the switching variable vector [a b c]Tand the line to line

output voltage vector [ VabVbcVca]Tand the phase voltage vector [Va Vb Vc]

Tis given

by the following equations.

[ ]= Vdc[ ]

(2.3)

[ ]= [ ] (2.4)


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Where Vab = Va * Vb, Vbc = Vb * Vc, Vca = Vc*Va. The stator - voltages

corresponding to the three phase voltages are:

Vsa= Va (2.5)

Vs=Va+ Vb (2.6)

The above equation can also be expressed in matrix form by using the equation

Va+Vb+Vc = 0.

[]=

(2.7)

There are eight (23) possible combinations for the switch commands. These

eight switch combinations determine the eight phase voltages vectors, of which the

results are six non zero vector (V1-V6) and two zero vectors(V0, V7) as shown inFigure 2.5.

Figure 2.5: Configuration Of Space Vector PWM


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The objective of space vector PWM technique is to generate the desired

instantaneous reference voltages from the corresponding basic space vectors based on

the switching states. Figure 2.5. It shows that the basic space vectors divide the plan

into six sectors. Depending on the sector that the reference voltages are in, two adjacent

basic vectors are chosen. The two vectors are time weighted in a sample period T

(PWM period) to produce the desired output voltage (Buja 2004).

Assuming that the reference vector Vout is in the sector 3 as shown in Figure 2

6a, the application time of two adjacent vectors is given by:

T = T4+T6+T0 (2.8)

=

+

(2.9)

Where T4and T6are the duration of the basic vectors V4and V6 to be applied

respectively. T0 is the duration for the zero vectors (V0 or V7). Once the reference

voltage Vout and the PWM period T are known, T4, T6 and T0 can be determined

according to the above equation (3).

T4=

(3 ) (2.10)

T6=

(2.11)

T0= T(T4+ T6) (2.12)

Where Vsa, Vs is - component of Vout. The voltage Voutis an approximation of

the desired output voltage based on the assumption that the change of output voltage is

negligible within a PWM period T.. Therefore, it is crucial that the PWM period is

small with respect to the change of Vout. In practice the approximation is very good

because the calureculation is performed in every PWM period (200s).


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2.4. ADAPTIVE PID REGULATOR

A motor drive based on the field oriented control needs two inputs; the torque

component reference isqref and the flux component reference isdref. The classical

proportional and integral (PI) regulator is often used to regulate the motor torque and

flux to the desired values(Leksono Edi 2000). This regulator which is implemented in

this thesis, is capable of reaching constant references by correctly setting both the P

term (Kp) and the I term(K1). The P term and I term respectively regulate the error

sensibility and steady state error. The regulation can be improved with the adaptive

proportional integral derivative (PID) regulator (Lin F.J 1999).

To design a digital PID controller for the motor control, it may first consider the

transfer function of an analog PID regulator:

D(s)= Kp +K1

+KD

s (2.13)

Where Kp is the proportional gain, Kt is the integral gain, and KD is the

derivative gain. Similar to the Laplace Transform in continues time domain, the

integrator and differentilator can be represented by pulse transfer function in discrete

domain.

Where Kp is the proportional gain, Kt is the integral gain, and KD is the

derivative gain. Similar to the laplace transform is continous time domain, the integrator

and differentiator can be represented by pulse transfer function in discrete domain.

Integrator=

(2.14)

Differentiator =

(2.15)

Where T is the sampling period. Thus the transfer function of a digital non

adaptive PID controller is


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D(z)= Kp+K1

+KD (2.16)

=

(2.17)

Where

a0 =Kp+

+

(2.18)

a1=-Kp+

+

(2.19)

a2=

(2.20)

Figure 2.6. It shows the block diagram of an adaptive control system. The r is

input or set point, cis the output feedback,yis the output and D(z) is the adaptive PID

controller. The adaptive control scheme consists of two parts. First, the regulator uses

initial (or updated) PID parameter and feedback input samples to determine theregulation. Second, the regular updates the PID parameters until the error signal e2are

approaching zero.

Figure 2.6: Adaptive PID control


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A quadratic objective function is used to minimize e2 with respect to the

regulator parameters.

f(a0,a1,a2) = (e2)2

= (z)]

2

= [ (

)]

2 (2.21)

The first order partial derivatives with respect to the regulator parameter a0,a1,a2are given below:

( )e1+ (

) (

)e12 (2.22)

Where a0,a1,a2 can be solved according to the steepest decent method of the

gradient techniques, so that the following equation can be obtained.

An(k+1) =an(k) +

n= 0,1,2; k=0,1,2 (2.23)

2.5. ARTIFICIAL INTELLIGENT CONTROL

Intelligent control is a class ofcontrol techniques, that use various AI computing

approaches likeneural networks, Bayesian probability, fuzzy logic,machine

learning,evolutionary computation and genetic algorithms(Qinghui Wu 2009). Before

we begin with what an intelligent control is, it is important to note what an intelligent

agent essentially means. An intelligent or a rational agent is one who simply does the

right things. This leads to a definition of an ideal rational agent: For each possible

percept sequence (Fitzgerald. A 2006), an Agent ideal rational agent should do whatever

action is expected to maximize its performance measure, on the basis of the evidence

provided by the percept sequence and whatever built-in knowledge the agent has.

Intelligent control achieves automation via the emulation of biological intelligence(B.K
http://en.wikipedia.org/wiki/Control_theoryhttp://en.wikipedia.org/wiki/Neural_networkshttp://en.wikipedia.org/wiki/Bayesian_probabilityhttp://en.wikipedia.org/wiki/Fuzzy_logichttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Evolutionary_computationhttp://en.wikipedia.org/wiki/Genetic_algorithmhttp://en.wikipedia.org/wiki/Rational_agenthttp://en.wikipedia.org/wiki/Rational_agenthttp://en.wikipedia.org/wiki/Genetic_algorithmhttp://en.wikipedia.org/wiki/Evolutionary_computationhttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Fuzzy_logichttp://en.wikipedia.org/wiki/Bayesian_probabilityhttp://en.wikipedia.org/wiki/Neural_networkshttp://en.wikipedia.org/wiki/Control_theory


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2007). It either seeks to replace a human who performs a control task (e.g., a chemical

process operator) or it borrows ideas from how biological systems solve problems and

applies them to the solution of control problems (e.g., the use of neural networks for

control).

Intelligent control can be divided into the following major sub-domains:

Neural network control

Bayesian control

Fuzzy (logic) control

Neuro-fuzzy control

Expert Systems

Genetic control

Intelligent agents (Cognitive/Conscious control)

New control techniques are created continuously as new models of intelligent

behavior are created and computational methods developed to support them.

Neural network controllers

Neural networks have been used to solve problems in almost all spheres of science

and technology. Neural network control basically involves two steps:

System identification

Controlling

It has been shown that afeed forward network with nonlinear, continuous and

differentiable activation functions haveuniversal approximation capability(Buja

2004). Recurrent networks have also been used for system identification. Given, a set

of input-output data pairs, system identification aims to form a mapping among these

data pairs. Such a network is supposed to capture the dynamics of a system(Qiang Lu

and Proceedings of the CSEE 2006).
http://en.wikipedia.org/wiki/Neural_networkhttp://en.wikipedia.org/wiki/Bayesian_probabilityhttp://en.wikipedia.org/wiki/Fuzzy_logichttp://en.wikipedia.org/wiki/Neuro-fuzzyhttp://en.wikipedia.org/wiki/Expert_Systemhttp://en.wikipedia.org/wiki/Genetic_controlhttp://en.wikipedia.org/wiki/Intelligent_agenthttp://en.wikipedia.org/wiki/Neural_networkshttp://en.wikipedia.org/wiki/Feedforwardhttp://en.wikipedia.org/wiki/Universal_approximation_theoremhttp://en.wikipedia.org/wiki/Recurrent_neural_networkhttp://en.wikipedia.org/wiki/Recurrent_neural_networkhttp://en.wikipedia.org/wiki/Universal_approximation_theoremhttp://en.wikipedia.org/wiki/Feedforwardhttp://en.wikipedia.org/wiki/Neural_networkshttp://en.wikipedia.org/wiki/Intelligent_agenthttp://en.wikipedia.org/wiki/Genetic_controlhttp://en.wikipedia.org/wiki/Expert_Systemhttp://en.wikipedia.org/wiki/Neuro-fuzzyhttp://en.wikipedia.org/wiki/Fuzzy_logichttp://en.wikipedia.org/wiki/Bayesian_probabilityhttp://en.wikipedia.org/wiki/Neural_network


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Artificial neural networks are a computational tool, based on the properties of

biological neural systems. Neural networks excel in a number of problem areas where

conventional von Neumann computer systems have traditionally been slow and

inefficient (Lascu C 2005). This thesis is discussed the creation and use of artificial

neural networks for give signal in trigger and make open thyristor to be gave current to

induction motor 3 phase.

Artificial Neural Networks, also known as Artificial neural nets, neural nets,

or ANN for short, are a computational tool modeled on the interconnection of the

neuron in the nervous systems of the human brain and that of other organisms.

Biological Neural Nets (BNN) are the naturally occurring equivalent of the ANN (Lee

K.B 2001). Both BNN and ANN are network systems constructed from atomic

components known as neurons. Artificial neural networks are very different from

biological networks, although many of the concepts and characteristics of biological

systems are faithfully reproduced in the artificial system. Artificial neural nets are a type

of non-linear processing system that is ideally suited for a wide range of tasks,

especially tasks where there is no existing algorithm for task completion. ANN can be

trained to solve certain problems using a teaching method and sample data. In this way,

identically constructed ANN can be used to perform different tasks depending on the

training received. With proper training, ANN is capable of generalization, the ability to

recognize similarities among different input patterns, especially patterns that have been

corrupted by noise(Mohamadian M 2003)

Most of the control theory developed with linear time invariant systems. And

powerful methods for designing controllers for such systems are currently available.However, as applications become more complex, the processes to be controlled are

increasingly characterized by uncertainty in the system model. Non-linarites, presence

of noise and effect of have distributed sensors and actuators with their associated delays

and other problems. One approach used for handling a non-linear system has been to

linearize it around an equilibrium point, and then use the well-established linear control

theory to study issues like stability, controllability, and design controllers to function in

an approximate linear region around the equilibrium point.


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In most cases where ANNs have been used in the control of induction motors,

online training has been preferred. On line training has the potential to adapt to

changing motor parameters, but it is computationally very expensive, and it is very

difficult to run an average sized ANN in real time with on line training.

On way to control a plant using ANNs is to train the ANN off line to mimic an

existing controller. This implies that the ANN must have as input, all the quantities that

are input to the existing controller (with suitable number of previous values)

The ANN is trained off line to produce the same outputs as the controller and

after sufficient training; the ANN should be able to replace the controller. A block

diagram of this scheme is shown in Figure 2.7.

Figure 2.7: Schemes of ANN for controller.

Fuzzy Logic controllers

Fuzzy control is a methodology to represent and implement a (smart) humans

knowledge about how to control a system. The fuzzy controller has several components:

The rule-base is a set of rules about how to control.

Fuzzification is the process of transforming the numeric inputs into a form that can be

used by the inference mechanism.


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The inference mechanism uses information about the current inputs (formed by

fuzzification), decides which rules apply in the current situation, and forms conclusions

about what the plant input should be.

Defuzzification converts the conclusions reached by the inference mechanism into a

numeric input for the plant.

Genetic Control

To understand what Genetic control is, one must suffice oneself with the theory of

genes and the importance of its study in an artificially intelligent machine Genes are the

blueprint of our bodies, a blueprint that creates the variety of proteins essential to any

organisms survival. These proteins, which are used in countless ways by our bodies,

are produced by genetic sequences, i.e. our genes, as described in the cell biology

section, protein synthesis pages. Utilization of Genetic Information All cells have

originated from the single zygote cell that formed it, and therefore possess all the

genetic information that was held in that zygote. This means that an organism could be

cloned from the genetic information in the nucleus of one cell, regardless of the volume

of cells that make the organism (be it one or billions).

However, this brings about the following question, how can cells become

differentiated and specialized to perform a particular function if they are all the same?

The answer to this is each cell performing its unique role has some of its genes

'switched on' and some 'switched off'.

In light of this, the cells in our body still contain the same genetic information,

though only a partial amount of this information is being used in any one cell. SwitchedOn and Switched off Some genes are permanently switched on, because they contain the

blueprint for vital metabolites (enzymes required for respiration etc). However, since

cells become specialized in multi-cellular organisms such as us, some genes become

switched off because they are no longer required to be functional in that particular cell

or tissue.


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For instance, insulin is produced in pancreas cells, which must have the gene that

codes for insulin switched on, and perhaps other genes that are un-related to the role of

the pancreas can be switched off.

Some other genes that will be functional during specialization determine the

physical characteristics of the cell, i.e. long and smooth for a muscle cell or indented

like a goblet cell.

Genetic control then transforms all of this available information into an algorithm to

design and texture genes for avoiding mitotic growth and/or identifying probable threats

to the proposed gene. This branch of science deals with protection, regeneration and

transformation of the analytical information collected.

Bayesian contr ollers

Bayesian probability has produced a number of algorithms that are in common use

in many advanced control systems, serving asstate spaceestimators of some variables

that are used in the controller.

TheKalman filter and theParticle filter are two examples of popular Bayesian

control components. The Bayesian approach to controller design requires often an

important effort in deriving the so-called system model and measurement model, which

are the mathematical relationships linking the state variables to the sensor

measurements available in the controlled system. In this respect, it is very closely linked

to thesystem-theoretic approach tocontrol design.

2.6 DIGITAL SIGNAL PROCESSING IMPLEMANTATION OF SPEED

SENSORLESS FOC

The fixed point DSP TMF28335 is the core of the control system designed in this

thesis. The following provides an overview of the DSP FOC operations.
http://en.wikipedia.org/wiki/Bayesian_probabilityhttp://en.wikipedia.org/wiki/State_space_(controls)http://en.wikipedia.org/wiki/Estimatorhttp://en.wikipedia.org/wiki/Kalman_filterhttp://en.wikipedia.org/wiki/Particle_filterhttp://en.wikipedia.org/wiki/Systems_theoryhttp://en.wikipedia.org/wiki/Control_engineeringhttp://en.wikipedia.org/wiki/Control_engineeringhttp://en.wikipedia.org/wiki/Systems_theoryhttp://en.wikipedia.org/wiki/Particle_filterhttp://en.wikipedia.org/wiki/Kalman_filterhttp://en.wikipedia.org/wiki/Estimatorhttp://en.wikipedia.org/wiki/State_space_(controls)http://en.wikipedia.org/wiki/Bayesian_probability


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- DSP operates its analog-to-digital converter (ADC) to collect the instantaneous

induction motor input currents measured by a current transducer at the motor

terminal.

-

DSP carries out a specific d-coordinate chosen to be in line with the rotor flux,

using the - currents and the flux angle, to compute the d-coordinate and q-

coordinate currents.

- DSP carries out an inverse d-q transformation, using the d-q reference voltages,

to compute the stator and reference voltages.

- DSP executes three feedback regulators for the motor speed, the rotor torque,

and the rotor flux, to determine the d-coordinate and the q-coordinate stator

reference voltages.

- DSP executed the space vector pulse width modulation (PWM) module, using

the - reference voltages, to compute the PWM control signals.

- DSP finally outputs the PWM control signal to the gating circuit of the power

electronic inverter that drives the induction motor.

2.6.1 Hardware Implementation of Field Oriented Control

Figure 2.8 shows the set up general of DSP block diagram of the hardware

required to implement a sensorless speed control system, for an induction motor.

The DSP control system designed in this Thesis consists of the following major

hardware components:

1. DSP controller

2.

Power Inverter3.

Induction Motor

Figure 2.8: Experimental Set up general of DSP


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DSP is the key control element in this thesis for design of the FOC of induction

motors. Setting DSP TMF28335 given as follows.

-

Development/Emulation: Code Composer 4.1. Support real time debugging.

- CPU clock: 30 MHZ.

-

PWM frequency :5 kHz.

- PWM mode: Symmetrical with the dead band 1,8s.

-

Interrupts

- System time base.

2.6.2 DSP Software Implementation of Field Oriented Control

DSP control software developed in this thesis is based on two modules: the

initialization and the run module (Song J 2000). The initialization module is performed

only once at the beginning of the software execution. The run module is based on a user

interface loop interrupted by the PWM underflow.

The benefits of structured modulator software are well known. This is especially

true for large complex system, such as the FOC motor control, with many sub-block. It

reduces the developing time, and could be reused in the future a project. Therefore, a

new method use DSP driver incremental build process is used in this research thesis.

2.7 SUMMARY

Modeling of Induction motor is the first and essential step for its identificationand control. The mathematical model of the machine should on one hand have such a

structure so as to completely describe the characteristics of the machine and on the other

hand, be convenient to use it for implementing estimation algorithms. In this chapter,

literature review is discussed. The chapter starts with parts of induction motor like

stator, rotor, squire cage and, etc., introduction of the reference frames. Then principal

operation.Differential equation, hardware DSP and software implementation.

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