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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.