fuzzy logic13

8/3/2019 Fuzzy Logic13

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Real Life Application of

Fuzzy Logic:

A Smart Traffic Light Controller

BY

N.V.BHARGAVA.G

K.I.T.S WARANGAL


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What is FUZZY LOGIC????

Fuzzy logic is basically a multi valued

logic that allows intermediate values to be definedbetween conventional or crisp evaluations likeyes/no, true/false, black/white etc.. Fuzzy logiccan be used to control household appliances suchas washing machines (which sense load size anddetergent concentration and adjust their washcycles accordingly) and refrigerators.


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HISTORY OF FUZZY LOGIC!!!!!

Fuzzy logic was conceived by Lotfi Zadeh,former chairman of the electrical engineering andcomputer science department at the University ofCalifornia at Berkeley. In 1965, whilecontemplating how computers could be

programmed for handwriting recognition, Zadehexpanded on traditional set theory by makingmembership in a set a matter of degree rather thana yes-no situation.

Professor Lotfi A. Zadeh


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FUZZY (vs) CRISP

Is water

colourless??

?????

CRISP

YES

NO

FUZZYIs Ram

honest?

Extremely

honest

Very honest

Honest at times

Extremely

dishonest


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Why use fuzzy logic?????

Fuzzy Logic offers several unique features that

make it a particularly good choice for many controlproblems.

It is inherently robust Easy to modify or tweaken to improve or drastically

alter system performance overall system cost and complexity lowBecause of the rule-based operation, any reasonable

number of inputs can be processed (1-8 or more)

and numerous outputs (1-4 or more) generated Fuzzy Logic can control nonlinear systems that

would be difficult or impossible to modelmathematically


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Fuzzy sets

The very basic notion of fuzzy systemsis a Fuzzy (sub) set. In classical mathematicswe are familiar with what we call crisp sets.

Here is an example:. Now, let's define a subset A of X

of all real-numbers in the range between 5 and 8. A =[5,8] We now show the set A by its membershipfunction, Membership function of set A

CRISP SET FUZZY SET


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STANDARD SHAPES OF MEMBERSHIP

FUNCTIONS

Triangular Trapezoidal

Gaussian


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Basic operations on fuzzy setsLetA be a fuzzy interval between 5 and8 andB be a fuzzy numberabout

4.


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Fuzzy rules

Fuzzy rules define fuzzypatches, which is the key idea

in fuzzy logic.

A machine is made smarter using a concept designed

by Bart Kosko called the Fuzzy

ApproximationTheorem(FAT). The FAT theorem

generally states a finite number of patches can cover a

curve as seen in the figure below. If the patches are large,

then the rules are sloppy. If the patches are small then the

rules are fine. Graphically, if the rule patches shrink, our

fuzzy subset triangles get narrower.. It is math-free system


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FUZZY CONTROLLER

A fuzzy controller contains three main components: Fuzzifier: Converts a crisp input from outside world into a fuzzy set

so that it can be operated by the system

Inference Engine: Contains a number of fuzzy logic rules called

membership functions (MFs) and determines the degree of truth of

the input based on these rules

Defuzzifier: Converts the inference engines fuzzy output into a crisp

value, which is the output of the system used for control

C S d ffi i h


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Case Study: Fuzzy Traffic Light

Controller

This part of the report describes the designprocedures of a real life application of fuzzy logic:A Smart Traffic Light Controller. The controller issuppose to change the cycle time depending upon

the densities of cars behind green and red lights andthe current cycle time.

. Obviously, a mathematical model for thisdecision is enormously difficult to find. However,with fuzzy logic, it is relatively much easier.


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Fuzzy Design

Eight incremental sensors are put in specificpositions .the first sensor behind each traffic lightcounts the number of cars coming to the intersection andthe latter counts number of cars.

. The distance D is chosen and is used to determine

the maximum density of cars allowed to wait in a verycrowded situation. This is done by adding the number ofcars between to paths and dividing it by the totaldistance. For instance, the number of cars between theEast and West street is

(s1-s2)+(s5-s6)/D.


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Fuzzy decision process

Step 1:As before, firstly the inputs and outputs of the design has

to be determined. Assuming red light is shown to both Northand South streets and distance D is constant, the inputs ofthe model consist of:

1) Cycle Time2) Cars behind red light

3) Cars behind green light

The cars behind the light are the maximum number ofcars in the two directions. The corresponding output

parameter is the probability of change of the current cycletime. Once this is done, the input and output parameters aredivided into overlapping member functions, each functioncorresponding to different levels.


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Step 2

The rules, as before are formulated using a

series of if-then statements, combined with

AND/OR operators.

Step 3

This process, also mentioned above converts

the fuzzy set output to real crisp value. The

method used for this system is center of gravity:

Crisp Output={Sum(Membership

Degree*Singleton Position)}/(Membership

degree


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Testing

The fuzzy controller is tested and itsperformance is evaluated to determine if it is the

best option when compared to a conventional

controller and human expert.

Testing of the controller: The fuzzy controller

has been tested under seven different kinds of

traffic conditions: from very heavy traffic to very

lean traffic. 35 random chosen car densities were

grouped according to different periods of the day

representing those traffic conditions


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Performance evaluation!!!

The performance of the controller was compared with that of aconventional controller and a human expert. The criteria used forcomparison were number of cars allowed to pass at one time and

average waiting time. All three traffic controller types were compared

and can be summarized with the following graph of performance

index in all seven traffic categories.


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Result of case study

The fuzzy controller passed through 31%

more cars, with an average waiting time shorter by5% than the theoretical minimum of the

conventional controller. The performance also

measured 72% higher. This was expected.

However, in comparison with a human expert thefuzzy controller passed through 14% more cars

with 14% shorter waiting time and 36% higher

performance index.

Result: Machine beats Man!


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Conclusion

FL was conceived as a better method for sortingand handling data but has proven to be a excellent

choice for many control system applications since it

mimics human control logic. It can be built into

anything from small, hand-held products to large

computerized process control systems. It uses animprecise but very descriptive language to deal with

input data more like a human operator. It is very robust

and forgiving of operator. In conclusion, as Man gets

hungry in finding new ways of improving our way oflife, new, smarter machines must be created. Fuzzy

logic provides a simple and efficient way to meet these

demands and the future of it is limitless


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