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```ILLUMINATION CONTROL
USING
FUZZY 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
APPLICATION OF ILLUMINATION CONTROL
SYSTEM
• CONCLUSION
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HUMAN LIKE THINKING
“THINKING”………………
* DIGITAL LOGIC
* FUZZY LOGIC
0 OR 1 (Y OR N)
FUZZY LOGIC: [0,1]
DIGITAL LOGIC:
HISTORIC
BACKGROUND
• Fuzzy logic was born in 1965
father of fuzzy logic – LOTFI
 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.
ARCHITECTURE OF FLC
DESIGN OF FLC
CLASSIFICATION 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
Z
[0,100];
[-100,100]
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
 NB
 NM
 NS
 ZE
 PS
 PM
-Negative Big
-Negative Medium
-Negative Small
-Zero
-Positive Small
-Positive Medium
 PB
-Positive Big
Output
DK -Dark
ST
-Streak
SP
-Spark
M
-Minimum
MD -medium
H
-High
Brightness
VH -Very High
Brightness
RANGES OF LINGUAL VARIABLE
Input lingual
 NB
 NS
 ZE
 PS
 PB
output lingual
 VH
 HI
 MD
 M
 DK
range
-100 - -45
-90 - 0
-45 - 45
0 - 90
45 - 100
range
0 - 35
20 - 50
35 - 65
50 - 80
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…
 Triangular
 Gaussion function
 ϒ function
 S function
Generally trianguler membership
function 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 iteration
If
If
If
If
(l1 = x1 AND l3 = y1) then U = Z1
(l1 = x1 AND l4 = y2) then U = Z2
(l2 = x2 AND l3 = y1) then U = Z3
(l2 = x2 AND l4 = y2) then U = Z4
•For the given input the lingual variable in which output will
lie is determined by knowledge and experience.
•Total 49 possible rule
•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.
Max-min composition
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
 most commonly used
COG = ∫zµdz
∫µdz
defuzzification method.
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Humen like thinking
Efficient design for non-linear control system
Cheaper
Reduces tedious mathematical calculation
Reliable
 FORMATION OF RULE IS VERY TEDIOUS
 OBEYS NEW LOGIC
APPLICATION OF ILLUMINATION
CONTROLLER
 sensitive photosynthesis
 LCD brightness control
 Street light
 Automatic room light control
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.
```