ILLUMINATION CONTROL USING FUZZY LOGIC

1. ILLUMINATION CONTROL USINGFUZZY LOGIC PRESENTED BY: VIVEK RAUNAK reg: 13090260

2. 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

3. INTRODUCTION HUMAN LIKE THINKING “THINKING”……………… * DIGITAL LOGIC * FUZZY LOGIC DIGITAL LOGIC: 0 OR 1 (Y OR N) FUZZY LOGIC: [0,1]

4. HISTORIC BACKGROUND LOTFI ZADEH • Fuzzy logic was born in 1965 father of fuzzy logic – LOTFI ZADEH • Fristly 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.

5. ARCHITECTURE OF FLC

6. DESIGN OF FLC • CLASSIFICATION AND SCALING OF INPUT(FUZZY PLANE) • FUZZIFICATION • RULE FORMATION • RULE FIRING • DEFUZZIFICZTION

7. 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

8. 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.

9. LINGUAL VARIABLES Input Output • NB -Negative Big DK -Dark • NM -Negative Medium ST -Streak • NS -Negative Small SP -Spark • ZE -Zero M -Minimum • PS -Positive Small MD -medium • PM -Positive Medium H -High Brightness • PB -Positive Big VH -Very High Brightness

10. RANGES OF LINGUAL VARIABLE Input lingual range • NB -100 - -45 • NS -90 - 0 • ZE -45 - 45 • PS 0 - 90 • PB 45 - 100 output lingual range • VH 0 - 35 • HI 20 - 50 • MD 35 - 65 • M 50 - 80 • DK 65 - 100

11. Membership function • It is function through which we get membership value of the element of lingual variable. • Ranges from 0 to 1. types… • Triangular • Gaussion function • ϒ function • S function Generally trianguler membership function is used.

12. FUZZY PLANE

13. FUZZIFICATION • It is process to change crisp input into fuzzy input.

14. Rule formation • “if(A=x) then (z=y)” antecedent conclusion • Rule formation needs knowledge and experiment. • 4 rules in single iteration If (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

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

16. Rule firing Rule firing mean…to apply the pre-determined rule to get the output. There are many methods for rule firing Minimum composition Product of maximum composition Maximum of minimum composition Minimum of minimum composition Maximum of maximum composition We use max-min composition for inferring output.

17. Max-min composition

18. Defuzzification • It is process to convert fuzzy output into crisp output. • Various method: • Centre of gravity defuzzification • Centre of sums defuzzification • Centre of largest area defuzzification • First of maxima defuzzification • Middle of maxima defuzzification • Height defuzzification

19. COG most commonly used defuzzification method. COG = ∫zµdz ∫µdz

20. Hardware description

21. ADVANTAGES OF FLC • Humen like thinking • Efficient design for non-linear control system • Cheaper • Reduces tedious mathematical calculation • Reliable DISADVANTAGES • FORMATION OF RULE IS VERY TEDIOUS • OBEYS NEW LOGIC

22. APPLICATION OF ILLUMINATION CONTROLLER • sensitive photosynthesis • LCD brightness control • Street light • Automatic room light control

23. CONCLUSION The 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.