This document presents an overview of a fuzzy logic control system for an automated accident prevention system in vehicles. It introduces fuzzy logic and how it is suited for control applications that involve imprecise human reasoning. It describes the differences between fuzzy and crisp sets, membership functions, fuzzification, and defuzzification. The document then provides an example of a fuzzy logic control system for braking that uses sensors to measure vehicle speed and distance to obstacles, defines input and output membership functions, and includes a set of rules to determine the braking level needed to prevent accidents. The conclusion states that such a fuzzy logic system can help relieve driver tension and prevent accidents, working towards an accident-free world.

1. FUZZY LOGIC
CONTROL SYSTEM
GUIDED BY
MRS. SANGITA PAL
PRESENTED BY
RAJANIKANTA PRADHAN
MCA 4TH SEM
IGIT Sarang
8895247580
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2. • Introduction
• Fuzzy Set vs. Crisp Set
• Membership Function
• Fuzzification
• Defuzzification
• Working principle
• Conclusion
• References
CONTENTS
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3. 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
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4. Fuzzy Set vs. Crisp Set
A classical set is defined by crisp boundaries.
A fuzzy set, on the other hand, is prescribed by ambiguous properties
resulting in ambiguous boundaries
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5. Membership Function & it’s features
characterizes the fuzziness in a fuzzy set
whether the elements in the set are discrete or continuous - in a
graphical form for eventual use in the mathematical formalisms of
fuzzy 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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6. 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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7. 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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8. 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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9. 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

10. Input Membership function for distance:
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11. Output - Membership Function For break:
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12. 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

13. Conclusion
An automated accident prevention system is necessary to prevent
accidents.
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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14. 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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15. AN ACCIDENT FREE WORLD
THANK YOU
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