1. ILLUMINATION CONTROL USING FUZZY 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.  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 E p [-100,100] ; ∆E p [-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 BigDK-Dark NM-Negative MediumST-Streak NS-Negative SmallSP-Spark ZE-ZeroM-Minimum PS-Positive SmallMD-medium PM-Positive MediumH-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. F UZZY 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 (l 1 = x 1 AND l 3 = y 1 ) then U = Z 1 If (l 1 = x 1 AND l 4 = y 2 ) then U = Z 2 If (l 2 = x 2 AND l 3 = y 1 ) then U = Z 3 If (l 2 = x 2 AND l 4 = y 2 ) then U = Z 4

15. 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 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.  most commonly used defuzzification method. COG = ∫zµdz ∫µdz

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.