UNIT V

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UNIT-5

APPLICATIONS OF FLC

www.Vidyarthiplus.com

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Fuzzy Logic Controller

• FLCs allow for a simpler human like approach to

control system design.

• They do not need the mathematical model of the

process.

• For non-linear systems, controlling with conventional

controllers is difficult.

• FLCs provide reasonable and effective alternatives to

classical controllers.


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Conventional Control System


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Fuzzy Logic Control System


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An Example

The following, illustrates a basic “fuzzy” automatic transmission

system. The transmission uses four fuzzy sensor inference inputs to

control the best gear selection for the given conditions. The inputs are

throttle, vehicle speed, engine speed and engine load.


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An Example (cont.)Labels and Membership Functions of Throttle.


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An Example (cont.)Labels and Membership Functions of vehicle speed.


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An Example (cont.)Labels and Membership Functions of engine speed.


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An Example (cont.)Labels and Membership Functions of engine load.


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An Example (cont.)Labels and Membership Functions of shift.


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An Example (cont.)Using the labels as defined in the previous slides, rules can be

written for the fuzzy interface unit. The rules provide a

tangible knowledge base required for the decision process

and are represented as English like if-then statements.


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An Example (cont.)

To create the fuzzy interface unit, rules such as the following would be developed to facilitate the automatic shifting of the vehicle.

If vehicle speed is low, variation of vehicle speed is small, slope resistance is positive large and accelerator is medium then mode is steep uphill mode.

If vehicle speed is medium, variation of vehicle speed is small,slope resistance is negative large and accelerator is small thenmode is gentle downhill mode.


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An Example (cont.)

If Mode is Steep uphill mode, the Shift is No. 2

If Mode is Gentle downhill mode, then Shift is No. 3

The previous slides illustrate how fuzzy logic can provide a powerful tool

when addressing complex situations that are not feasible using

conventional approaches. By employing fuzzy logic, we have the ability to

include additional variables and rules to take into account factors that

might improve the behavior of the control system.


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Fuzzy Anti-Lock Braking System

• ABS ensures optimal vehicle control and minimal stopping distances during hard or emergency braking irrespective of road and weather conditions.

• Experts predict that 35% to 50% of all cars built worldwide in five years will have ABS as standard equipment.

• Electronic control units (ECUs), wheel speed sensors, and brake modulators are major components of an ABS module.

• Wheel speed sensors transmit pulses to the ECU with a frequency proportional to wheel speed.


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Fuzzy ABS

• The ECU then processes this information and regulates the brake accordingly.

• Since ABS systems are nonlinear and dynamic in nature they are a prime candidate for fuzzy logic control.

• Intel & Inform Software Corporation Fuzzy ABS utilizes a high performance, low cost, 16-bit 8XC196Kx architecture to take advantage of improved math execution timing.

• Uses Fuzzy Rules like "If the rear wheels are turning slowly

and a short time ago the vehicle speed was high, then reduce rear brake pressure".


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ABS Block Diagram


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Inputs:1. The Brake: This block represents the brake pedal deflection/ assertion.

This information is acquired in a digital or analog format.2. The 4 W.D: This indicates if the vehicle is in the 4-wheel-drive mode. 3. The Ignition: This input registers if the ignition key is in place, and if the

engine is running or not.4. Feed-back: This block represents the set of inputs concerning the state of

the ABS system. 5. Wheel speed: In a typical application this will represent a set of 4 input

signals that convey the information concerning the speed of each wheel. This information is used to derive all necessary information for the control algorithm

Outputs:The proposed system shown above has two types of outputs. The PWM signals to control ABS braking, and an Error lamp signal to indicate a malfunction if one exists.


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Fuzzy Washing Machine

• The length of wash time is based on the amount of clothes you wish to wash and the type and degree of dirt you have.

• To automate this process, we use sensors to detect these parameters and the wash time is then determined from this data.

• There is no easy way to formulate a precise mathematical relationship between volume of clothes and dirt and the length of wash time required.

• There are two inputs: (1) one for the degree of dirt on the clothes and (2) one for the type of dirt on the clothes. These two inputs can be obtained from a single optical sensor.


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• The degree of dirt is determined by the transparency of the

wash water. The dirtier the clothes, the lower the transparency

for a fixed amount of water.

• The type of dirt is determined from the saturation time, the

time it takes to reach saturation.

• The fuzzy controller output is the length of the wash time.


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Fuzzy Controller


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Labels and Membership Functions of Input Variable dirtiness


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Labels and Membership Functions of Input Variable

type_of_dirt


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Labels and Membership Functions of Output Variable

wash_time


Inverted Pendulum Fuzzy Control

fuzzyControlFor2To1

provides the control to

keep the stick stable

Linearized inverted

pendulum on a cart

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Modeling the Inverted Pendulum

M mass of the cart 0.5 kg

m mass of the pendulum 0.5 kg

b friction of the cart 0.1 N/m/sec

l length to pendulum center of mass 0.3 m

I inertia of the pendulum 0.006 kg*m^2

F force applied to the cart

x cart position coordinate

theta pendulum angle from vertical

Source: http://www.engin.umich.edu/group/ctm/examples/pend/invpen.html

Moment of Inertia: http://hyperphysics.phy-astr.gsu.edu/hbase/mi2.html#rlin

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Inverted Pendulum – Swing Up Non-linear Model

Original Model

sin cos

=inertia of rod

=mass of rod

=gravity of rod

=half length of rod

=horizontal force

d

dt

dJ mgl mlu

dt

J

m

g

l

u

θω

ωθ θ

=

= − +

source: http://www.control.lth.se/publications/fulldocs/ast_fur96.pdf

Dimensionless model

sin ( , ) cos

( , ) if 0

0,otherwise

turnoff

d

dt

dgain

dt

gain k

θω

ωθ θ ω θ

θ ω π θ θ ω

=

= − +

= ≤ ≤ ∧ >

=

reset theta to 0

when reach 2*pi

detect when

pendulum stops

rising

detect when angle

reaches turn-off

level

detect when

pendulum stops

risingturnoff

θ θ≤

starting from hanging configuration, rod can be made to reach inverted configuration with

sufficient force acting until horizontal line is reached

0

π

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Fuzzy Logic Control of Blood Pressure

During Anesthesia

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INTRODUCTION

-The techniques of fuzzy logic and expert system have been used

in the medical area since middle 1970.

The anesthetists control important control variables such as

blood pressure, heart rate, temperature, blood oxygenation

and exhaled CO2 within the acceptable bounds. Anesthesia

must be maintained during the entire surgical procedure . The

goal is to develop automated control systems to regulate the

depth of anesthesia.

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ANESTHESIA CONTROL

1- A PID CONTROLLER

2- A FUZZY CONTROLLER

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1- PID CONTROLLER

Where the patient's transfer functions P(s) was cited in the following reference

)8.4

1)(

08.3

1)(

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1(

)6.10

1(

)(

+++

+

=

sss

sK

sP

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1- Proportional:-

If the difference between the current plant output and its desired value (the current error) is large, the software should probably change the drive signal a lot. If the error is small, it should change it only a little .

Error = P * (desired - current)

P = constant Proportional gain

2- Differentiation:-

The biggest problem with proportional control alone is that you want to reach new desired outputs quickly and avoid overshoot and minimize ripple once you get there. Responding quickly suggests a high proportional gain; minimizing overshoot and oscillation suggests a small proportional gain. Achieving both at the same time may not be possible in all systems.

D * (current -- previous)

where D is a constant derivative gain

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3- Integration

A remaining problem is that PD control alone

will not always settle exactly to the desired

output. In fact, depending on the proportional

gain, it's altogether possible that a PD

controller will ultimately settle to an output

value that is far from that desired.

I = Σ ( desired – current )

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PID with disturbances

In this section a disturbance is added to the system. Figure shows the effect of the disturbances.

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2- A FUZZY CONTROLLER

-The modeling of real world systems, however,

often presents problems. As processes

increase in complexity, they become less

amenable to direct mathematical modeling

based on physical laws since they may be

distributed, stochastic, non-linear and time-

varying, uncertain, etc.

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THE FUZZY LOGIC CONCEPT

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FUZZY SETS

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FUZZY OPERATIONS

1 1 -- xxmax(x,ymax(x,y))min(x,ymin(x,y))yyxx

1100000000

1111001100

0011000011

0011111111

0.80.80.50.50.20.20.50.50.20.2

0.30.30.70.70.20.20.20.20.70.7

0.40.40.60.60.60.60.60.60.60.6

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FUZZY CONTROL

There is more to fuzzy logic than some interesting math, it has some impressive applications in engineering. The main application of fuzzy logic in engineering is in the area of control systems. The definition of a control system, given by Richard Dorf in Modern Control Systems is: "An interconnection of components forming a system configuration that will provide a desired response." This means that a control system needs to know the desired response (input) and it needs to process this input and attempt to achieve it. The general control system can then be summarized with the following diagram .

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ANFIS: Matlab Fuzzy Logic toolbox

-You can create and edit fuzzy inference systems with Fuzzy Logic

Toolbox software. You can create these systems using graphical

tools or command-line functions, or you can generate them

automatically using either clustering or adaptive neuro-fuzzy

techniques.

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CONCLUSION

-In this project we applied a new algorithm using fuzzy logic controller for the control of unconsciousness via blood pressure measurements.

-A simulation platform was built around a non-linear recirculatory physiological model which was modified to include a more efficient way of delivering the anesthetic.

-The simulation results showed that the fuzzy-based algorithm was effective in terms of set-point tracking and drug consumption.

-A comparison with the PID controlled is carried out and the performances of the FLC over the PID are shown.

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Introduction

• Neuro-fuzzy systems

– Soft computing methods that combine in various

ways neural networks and fuzzy concepts

• ANN – nervous system – low level perceptive

and signal integration

• Fuzzy part – represents the emergent “higher

level” reasoning aspects

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Introduction

� “Fuzzification” of neural networks

� Endowing of fuzzy system with neural learning features

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Introduction

• Co-operative-neural algorithm adapt fuzzy systems

– Off-line – adaptation

– On-line – algorithms are used to adapt as the system operates

• Concurrent – where the two techniques are applied after one

another as pre- or post-processing

• Hybrid – fuzzy system being represented as a network structure,

making it possible to take advantage of learning algorithm inherited from

ANNs

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NEFPROX

• The input units are labelled x1..xn, hidden rule

units are called R1…Rk and the output units

are denoted as y1 ym

• Each connection is weighted with a fuzzy set

and is labelled with a linguistic term

• Connection coming from the same input unit

and having same label are weighted by the

same common weight (shared weight). The

same holds for the connections that lead to

the same output unit

• There is no pair of rules with identical

antecedents

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NEFPROX – learning

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NEFPROX – learning

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UNIT V

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