Fuzzy Logic Anti Lock Brake System

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International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012

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FUZZY LOGICANTI-LOCK BRAKE SYSTEM

Author: Darshan Modi, Co-Author: Zarana P adia, Kartik P atel

ABSTRACT:

In rece nt years fuzzy logic co ntrol techniques h ave b een applied to a w ide rang e o f systems. This paper presentation deals w ith s tudy

antilock-braking system (ABS) using fuzzy logic. Vehicle d ynamics an d braking systems are com plex an d behave st rongly n on-linear w hich

causes d ifficulties i n developing a cl assical controller f or ABS. Many el ectronic co ntrol systems in the au tomotive industry su ch as automatic

transmissions, engine c ontrol and Anti-lock Brake Systems ( ABS) realize su perior characteristics thr ough the s et of fuzzy l ogic b ased control

rather t han traditional control algorithms. ABS is n ow accepted as an essential contribution to vehicle s afety a nd speed estimation using

fuzzy l ogic. Intel Corporation are l eading supplier of Fu zzy L ogic t ools a nd systems. The use of fuzzy - logic i n conjunction with

microcontrollers i s a fairly n ew development in automotive a pplications. Improving ABS capability is a mutual goal of automotive

manufacturers an d Intel Corporation. In future i t is ex pected that ABS will be i mplemented all over t he w orld.

Fuzzy logic introduction revolutionizedhem odelingofABS. Now ABS Design has become m uch simpler due to the p redesigned

microcontrollers o f Intel technology.

Fuzzy ABS introduction lead to the ideas o f introducing FUZZY LOGIC into m any Automobile ap plications.

A Smart Traffic Light Controller. The co ntroller is su ppose t o c hange the cycle t ime d epending upon the densities o f cars beh ind green and

red lights a nd the c urrent cycle t ime.

Simple C ase S tudy: An Adaptive Fuzzy Air Conditioner
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As a n example, consider an a ir-conditioner con troller, which controls t he s peed of its m otor according to the t emperature o f the s urrounding.

Database s ystems are on e of the mostly used sources o f information that keep the vast amount of information. We h ave to b e ab le to p rocess

and convert it into a f orm that meets t he specic information needs. And this i s a problem because the current search techniques use real

values q uestions. The res ult is s hort or l ong list of ob jects which satisfy the conditions. This l ist i s to b e re-evaluated unless i s w orthless.

One o f the p ossible so lutions i s t o u se fuzzy l ogic in s earching databases.

INTRODUCTION:

Formal control logic i s b ased in the teachings o f Aristotle, where an element either i s o r is n ot a m ember of a p articular set , L. A . Zadeh was one of t hose

who investigated alterna tive forms of da ta classication. T he result of t his investigation was the introduction of f uzzy sets a nd fuzzy theory a t the

University o f California B erkeley in 1 965. Fuzzy l ogic, a m ore g eneralized data set, allows f or a "cl ass" w ith c ontinuous m embersh ip g radations. This f orm

of classication with d egrees o f membersh ip o ffers a much w ider scope o f applicability, especially in c ontrol applications. fuzzy l ogic t echniques have

been applied to a w ide range of systems, with m any e lectronic co ntrol systems i n t he automotive i ndustry, such as automatic t ransmission, engine control

and antilock b raking systems (A BSs) curr ently b eing pursued, particularly i n the United States. Fuzzy Control, a relatively n ew, intelligent, knowledge

based control technique performs e xceptionally w ell in nonlinear, complex and even in not mathematically d escribable systems. "If temperature i s H ot

and Time of Day is N oon then air conditioning equals ve ry h igh." Several rules, similar t o the example, cou ld be used to describe a system and controlled

response. The parameters of Hot, Time a nd Very H igh are d ened by m embership functions. As l inguistic d escriptions o f a s ystem are m uch easier t o

produce than c omplex m athematical models, fuzzy l ogic h as g reat appeal for con trolling complex sy stems a s ch anges in the system have little i f any

effect upon the algorithm. The output of a f uzzy l ogic syst em is de termined in o ne o f several ways. The Center O f Gravity (COG) technique w ill be

discussed in this p aragraph. O nce all rules are e valuated, t heir outputs a re combined in order to provide a si ngle value that will be defuzzied. This

output calculation is p erformed a s f ollows. The c ontrol rule output value is m ultiplied by its p osition a long the X-axis, yielding position times w eight for the

rule. Th is ca lculation is r epeated for all control rules.Theseposition/weight products a re combined t o form the s um of products. This su m of the p roducts

is di vided b y the sum of output values t o d etermine the COG output along the X-axis. COG is t he nal system output in a control algorithm.

MANUSCRIPT TITLE:

FUZZY LOGIC STRUCTURE: A FUZZY LOGIC CONTROLLER IS DESIGNED FOR AN ANTI-LOCK BRAKING SYSTEM (ABS).

KEYWORDS:

HEADINGS:

SUBHEADING:


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MAIN TEXT:


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ANTI-LOCK BRAKE SYSTEM:

The reason for the e!e"o#$ent of ant%"o&' (ra'es %s %n essen&e !er) s%$#"e* Un er (ra'%n+, %f one or $ore of a!eh%&"e s .hee"s "o&' /(e+%ns to s'% 0 then th%s has n $(er of &onse en&es3

(ra'%n+ %stan&e %n&reases*

steer%n+ &ontro" %s "ost

t%re .ear .%"" (e a(nor$a"* The o(!%o s &onse en&e %s that an a&&% ent %s far $ore "%'e") to o&& r* Thea##"%&at%on of (ra'es +enerates a for&e that %$#e es a !eh%&"es $ot%on () a##")%n+ a for&e %n the o##os%te

%re&t%on* 4 r%n+ se!ere (ra'%n+ s&enar%os, a #o%nt %s o(ta%ne %n .h%&h the tan+ent%a" !e"o&%t) of the t%re s rfa&ean the !e"o&%t) roa s rfa&e are not the sa$e* Th%s ter$ %s referre to as s"%# rat%o an %s the rat%o (et.een thetan+ent%a" !e"o&%t) of the t%re an the tr e +ro n s#ee of the !eh%&"e an %s e5#resse as3 6+ %s &%r& $ferent%a"!e"o&%t) of (ra'e .hee" an 6t %s !eh%&"e roa s#ee * 78 %s s"%# of free ro""%n+ .hee" an 1778 for "o&'e .hee"*

The (ra'%n+ for&e or the a hes%on &oe9&%ent of (ra'%n+ for&e / $f0 $eas re %n the %re&t%on the .hee" %s t rn%n+%s f n&t%on of s"%#*$f e#en s on a n $(er

6+ %s &%r& $ferent%a" !e"o&%t) of (ra'e .hee" an 6t %s !eh%&"e roa s#ee * 78 %s s"%# of free ro""%n+ .hee" an1778 for "o&'e .hee"* The (ra'%n+ for&e or the a hes%on &oeff%&%ent of (ra'%n+ for&e /:f0 $eas re %n the

%re&t%on the .hee" %s t rn%n+ %s f n&t%on of s"%#* :f e#en s on a n $(er of fa&tors, an the $a%n ones are3

roa s rfa&e $ater%a" &on %t%on

t%re $ater%a", %n;at%on #ress re, trea e#th, trea #attern an &onstr &t%on*

The a%$ of an ABS %s to $%n%$%


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a f n&t%on of .hee" s"%#*


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FUZZY ARCHITECTURE:

The e!e"o#$ent s)ste$, &a""e f


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A f "") +ra#h%&a" CASE too" that s ##orts a"" es%+n ste#s for f


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2 in/ 1 out

2 in/1 out

2 in/1 out

3 in/1 out

0.22ms

0.33ms

0.34ms

0.50ms

The Fuzzy-ABS Algorithm:

The F


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.%th

.hee" s#ee

! hee" an !eh%&"e s#ee !F


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F%+ re str &t re of f


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It sho " (e note that "%n+ %st%& !ar%a("es an r "e ta("es &an (e es%+ne .%th n $er%&a" o#t%$%


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D

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%n&rease s%+n% &ant")* h%"e the &ontro" &)&"e t%$e .as set to a stan ar !a" e of $se&, the &o$# tat%on t%$e.as on") 7* $se& Th%s o=ers fa&%"%t%es for %$#"e$entat%on of e5ten e !eh%&"e )na$%&s &ontro" * The ;e5%(%"%t)of the &o#ro&essor %s &ons% era("e, # to D? r "e (ases are #oss%("e, ea&h of the$ ha!%n+ # to 2 D %n# ts anr "es* F rther$ore an %nterfa&e to $ost &o$$on") se $%&ro#ro&essors %s a!a%"a("e* Ar(%trar) sha#es of$e$(ersh%# f n&t%ons, %=erent ef


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FUTURE WORKS:

In F t re %t %s e5#e&te that th%s te&hno"o+) of FUZZY ABS $ st (e %$#"e$ente %n e!er) #art of the .or" * Thesa+e of f


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Bibliography:

M%ts (%sh% Un!e%"s Ne. Ga""antVV,*


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The ABS Ra&e Is OnVV, ar s A to .or" , Ma)1

S een ra ' $ar K*, Les"%n 6er+hese, K* K* Maha#atra

C@ARLY GULLETT

4A6I4 ELTING an ROBERT KO ALCZYK

MO@AMME4 FENNIC@

BERT @ELLENT@AL

4a%W, A* an K%en&'e, U*3 Est%$at%on of 6eh%&"e S#ee - F


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Fuzzy Logic Anti Lock Brake System

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