fuzzy logic control system

FUZZY LOGIC CONTROL SYSTEM

GUIDED BY

MRS. SANGITA PAL

PRESENTED BY

RAJANIKANTA PRADHAN

MCA 4TH SEM

IGIT Sarang

8895247580

1

• Introduction

• Fuzzy Set vs. Crisp Set

• Membership Function

• Fuzzification

• Defuzzification

• Working principle

• Conclusion

• References

CONTENTS

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Fuzzy logic is best suited for control applications

The ability to embed imprecise human reasoning and complex

problems is the criterion by which the efficiency of fuzzy logic is

judged.

Fuzziness describes the ambiguity of an event. But not the uncertainty

in the randomness

Introduction

3

Fuzzy Set vs. Crisp Set

A classical set is defined by crisp boundaries.

A fuzzy set, on the other hand, is prescribed by ambiguous propertiesresulting in ambiguous boundaries

4

Membership Function & it’s features

characterizes the fuzziness in a fuzzy set

whether the elements in the set are discrete or continuous - in agraphical form for eventual use in the mathematical formalisms offuzzy set theory.

The core of a membership function (x) =

1.

The support is given by A(x) > 0.

Boundaries are given by 0 < A (x) < 1.

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Fuzzification

Fuzzification is the process of making a crisp quantity fuzzy.

They carry considerable uncertainty.

If the form of uncertainty arises because of imprecision or fuzziness,it can be represented by a membership function.

institution method is used for fuzzification of the input variables.

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Defuzzification is the conversion of a fuzzy quantity to a precise quantity.

Defuzzification techniques :

1. Max - Membership Principle:

known as height method is limited to peaked output junctions. Given by

c (Z*) (Z) for all z C

2. Centroid Method:

also called center of area, center of gravity given by

Z* =

Defuzzification

(Z)dzCμ

(Z).zdzCμ

~

~

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3. Weighted Average Method:

It’s valid for symmetrical O/P membership function. Given by

Z* = where denotes an algebraic sum.

4. Means-Max Membership: ( middle of maxima )

The MAX membership can be a plateau rather than a single point. Given by

Z* =

)z(cμ

z).z(cμ

~

~

2

ba

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Obstacle Sensor Unit

• Sensing Distance:

The sensing distance depends upon the speed of the car. speed can be controlled by gradual anti skid braking system.

Input Membership Function for velocity

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Fuzzy logic control system

Input Membership function for distance:

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Output - Membership Function For break:

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Set of rules for breaking system

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0 10 20 30 40 50 60 70 80 90 100

VS 1 .7 0.5 0 0 0 0 0 0 0 0

S 0 0 0.5 1 0.5 0 0 0 0 0 0

F 0 0 0 0 0 0.5 1 0.5 0 0 0

VF 0 0 0 0 0 0 O 0 0.5 1 0.5

VELOCITY

0 10 20 30 40 50 60 70 80 90 100

VN 1 .7 0.5 0 0 0 0 0 0 0 0

N 0 0 0.5 1 0.5 0 0 0 0 0 0

F 0 0 0 0 0 0.5 1 0.5 0 0 0

VF 0 0 0 0 0 0 O 0 0.5 1 0.5

DISTANCE

Conclusion

An automated accident prevention system is necessary to preventaccidents.

The fuzzy logic control system can relieve the driver from tension &prevents accidents.

This fuzzy control unit results in an accident free world.

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References

• Timothy J. Ross “Fuzzy logic for Engineering Applications”, 2nd

edition, Pearson education

• http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?reload=true&punumber=91

•

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AN ACCIDENT FREE WORLD

THANK YOU15