ILLUMINATION CONTROL USINGFUZZY LOGIC

PRESENTED BY: VIVEK RAUNAK reg:

13090260

CONTENTS• INTRODUCTION OF FUZZY LOGIC• HISTORIC BACKGROUND• ILLUMINATION CONTROL SYSTEM• ARCHITECTURE OF FLC• DESIGN STEPS OF FLC• HARDWARE DESCRIPTION • ADVANTAGE OF FLC• DISADVANTAGE OF FLC• APPLICATION OF ILLUMINATION CONTROL

SYSTEM• CONCLUSION

INTRODUCTIONHUMAN LIKE THINKING

“THINKING”……………… * DIGITAL LOGIC * FUZZY LOGIC

DIGITAL LOGIC: 0 OR 1 (Y OR N)FUZZY LOGIC: [0,1]

HISTORIC BACKGROUND

LOTFI ZADEH

• Fuzzy logic was born in 1965 father of fuzzy logic –

LOTFI ZADEHFristly used in control system

in 1974 by - EBRAHAM MAMDANI • The international fuzzy system

association (IFSA) was established in 1984

• It is too much famous in japan.

laboratory of international fuzzy engineering (LIFE)

was inaugurated in 1989.

ARCHITECTURE OF FLC

DESIGN OF FLCCLASSIFICATION AND SCALING

OF INPUT(FUZZY PLANE)

FUZZIFICATION

RULE FORMATION

RULE FIRING

DEFUZZIFICZTION

CLASSIFICATION AND SCALING OF INPUT input error = set point –

actual

Change in error = pre error - current error

Ep=(error / setpoint)100∆Ep =(change in error /

pre. error ) 100

DYNAMIC RANGE Ep [-100,100] ; ∆Ep [-

100,100] Z [0,100];

LINGUAL VARIABLE Fuzzy variable are called lingual

variable. It may have infinite no. of values, each value is

associated with distinct membership value.

LINGUAL VARIABLES Input Output NB -Negative Big DK -DarkNM -Negative Medium ST -Streak

NS -Negative Small SP -SparkZE -ZeroM -MinimumPS -Positive Small MD -mediumPM -Positive Medium H -High

BrightnessPB -Positive Big VH-Very High

Brightness

RANGES OF LINGUAL VARIABLE

Input lingual rangeNB -100 - -45NS -90 - 0

ZE -45 - 45PS 0 - 90 PB 45 - 100

output lingual rangeVH 0 - 35HI 20 - 50MD 35 - 65M 50 - 80DK 65 - 100

Membership function• It is function through which we get membership

value of the element of lingual variable. Ranges from 0 to 1. types…TriangularGaussion functionϒ function S function

Generally trianguler membershipfunction is used.

FUZZY PLANE

FUZZIFICATION It is process to change crisp input into fuzzy

input.

Rule formation “if(A=x) then (z=y)” antecedent conclusion

Rule formation needs knowledge and experiment.

4 rules in single iterationIf (l1 = x1 AND l3 = y1) then U = Z1

If (l1 = x1 AND l4 = y2) then U = Z2

If (l2 = x2 AND l3 = y1) then U = Z3

If (l2 = x2 AND l4 = y2) then U = Z4

Rule matrix•For the given input the lingual variable in which output will lie is determined by knowledge and experience.•Total 49 possible rule

Rule firing•Rule firing mean…to apply the pre-determined rule to get the output.There are many methods for rule firingMinimum compositionProduct of maximum compositionMaximum of minimum compositionMinimum of minimum compositionMaximum of maximum composition

•We use max-min composition for inferring output.

Max-min composition

DefuzzificationIt is process to convert fuzzy output into

crisp output.Various method:Centre of gravity defuzzificationCentre of sums defuzzificationCentre of largest area defuzzificationFirst of maxima defuzzificationMiddle of maxima defuzzificationHeight defuzzification

COG most commonly used defuzzification method.

COG = ∫zµdz ∫µdz

Hardware description

ADVANTAGES OF FLCHumen like thinkingEfficient design for non-linear control systemCheaperReduces tedious mathematical calculationReliable

DISADVANTAGES

FORMATION OF RULE IS VERY TEDIOUS

OBEYS NEW LOGIC

APPLICATION OF ILLUMINATION CONTROLLERsensitive photosynthesis LCD brightness controlStreet lightAutomatic room light control

CONCLUSIONThe Presentation aimed towards fuzzy logic

control system. we saw all aspects of FLC by taking a control system used for illumination control. Illumination control system controls the environment wherevere unpredictable change in illumination is expected.