FUZZY LOGIC CONTROLLER AND ITS APPLICATION ON WASHING MACHINE

FUZZY LOGIC CONTROLLER AND APPLICATIONSPRESENTED BY:ADITYA SHAW(13000312046)AYUSH SHARMA(13000312062)DEBIPRASAD MONDAL(13000312067)MH. SHAHIN AHMED(13000312085)

UNDER THE GUIDANCE OFMrs A.CHAKRABORTY(MENTOR)CONTENTSINTRODUCTIONHISTORYLOGICAL SETFEATURES OF FUZZY LOGICAPPLICATIONSFUZZY LOGIC BASED WASHING MACHINESUMMARY

INTRODUCTION:Todays most successful technologies for developing sophisticated control system.

Addresses applications perfectly resembling human decision.

Fills an important gap in engineering design methods.HISTORY:1965 Seminal Paper Fuzzy Logic by Prof. Lotfi Zadeh .

1970First Application in Control Engineering

1980Empirical Verification in Europe

2000Becomes a Standard Technology.

WHAT IS FUZZY LOGIC?DEFINITION:Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between "completely true" and "completely false".

FUZZY LOGIC REPRESENTATION:

SlowestFastestSlowFast[ 0.0 0.25 ][ 0.25 0.50 ][ 0.50 0.75 ][ 0.75 1.00 ]7

7CLASSICAL & FUZZY SET567X (height)c (x) = {A1 x A0 x AClassical Set (Crisp)Contain objects that satisfy precise properties of membership.

Example: Set of heights from 5 to 7 feetFuzzy SetContain objects that satisfy imprecise properties of membershipExample : The set of heights in the region around 6 feetMembership Function10567X (height)A = {x, A(x) | x X}Where,A Fuzzy Setx Elements of XX Universe of DiscourseA(x) Membership Function of x in AFUZZY SET REPRESENTATION:EXAMPLE OF FUZZY LOGIC:

(IF) TEMPERATURE (THEN) FAN VERY COLD STOP COLD TURN DOWN NORMAL MAINTAIN LEVEL HOT SPEED UP FANFUZZINESS VERSUS PROBABILITY:

SALIENT FEATURES OF FUZZY LOGIC:Conceptually easy to understand.

Based on natural language.

Tolerant of imprecise data.

Can model nonlinear functions of arbitrary complexity

Fuzzy control systemFUZZIFICATION: Using membership functions to graphically describe a situation.

RULE EVALUATION: Application of Fuzzy Rules.

DEFUZZIFICATION: Obtaining the crisp or actual results.

PROCESSES:

BLOCK DIAGRAM:HOW FUZZY WORKS:Takes information in normal language and converts it into values.

Values given in form of words such as small, smallest.

Input and output variables are determined as less, much, hot and cold etc.

Outputs produced by using fuzzified inputs and rules are determined.

Fuzzified outputs converted to real values (defuzzification).

APPLICATIONS OF FUZZY LOGIC:Automatic Control System

User Interface and neural language processing

Domestic appliances and embedded system

Criminal Search SystemWASHING MACHINE BASED ON FUZZY LOGICSaves effort in brushing, agitating and washing the cloth.

Different type of cloth need different amount of washing time

BLOCK DIAGRAM:PARAMETERS USED:INPUT VARIABLES:

Degree of Dirt: Determined by transparency of the wash water.

Type of Dirt: Determined by time of saturation.

OUTPUT VARIABLES:

Wash Time: Depends on the input variables Degree of dirt & type of dirt.

SET OF FUZZY RULES USED :DIRTNESS OF CLOTHESTYPE OF DIRTWASHING TIMELARGEGREASYVERY LONGMEDIUMGREASYLONGSMALLGREASYLONGLARGEMEDIUMLONGMEDIUMMEDIUMMEDIUMSMALLMEDIUMMEDIUMLARGENOT GREASYMEDIUMMEDIUMNOT GREASYSHORTSMALLNOT GREASYVERY SHORT

FUNCTION OF DIRTNESS OF CLOTHES:

FUNCTION OF TYPE OF DIRT:

FUNCTION FOR WASH TIME:SUMMARY:Provides an alternative way to represent linguistic attributes of the real world in computing.

Improves the efficiency and simplicity of the design process.

Fuzzy sets are sets with soft boundaries

Performs interference on fuzzy sets

Applied in a variety of areas

Future developments are likely to be concerned with uncertainty model.BIBLIOGRAPHY: A. Zadeh The birth and evolution of fuzzy logic Journal Soft. Vol.2, No.1, 1990www.wikipedia.org\fuzzylogics. html http://www.ics.uci.edu/_mlearn/ MLRepository http://www.youtube.com/fuzzy+logichttp://www.quora.com/What-are-good-real-world-examples-of-fuzzy-logic-being-usedThank you

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