2. CONTENTS
INTRODUCTION
BASIC CONCEPTS OF FUZZY LOGIC
WORKING
WHY USE FUZZY LOGIC?
STEPS IN FUZZY LOGIC CONTROL
APPLICATIONS
APPLICATIONS TO MANUFACTURING
CONCLUSION
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3. INTRODUCTION
Fuzzy meaning vague
First proposed in 1965 by Lotfi Zadeh as a
way to process imprecise data
WHAT IS FUZZY LOGIC?
1. ZADEH – “Attempt to mimic human control
logic”
2. deals with approximate values rather than fixed
and exact
3. A form of many-valued logic
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4. BASIC CONCEPTS OF FUZZY LOGIC
In Conventional (Boolean) logic, binary sets
have only 2 values- true (1) or false (0)
A Fuzzy variable may have truth value ranging
between 0 and 1
Considers partial truth i.e. truth value may range
between completely true and completely false
Example :- How red is this? ½? ¾? 1?
RGB value 150/255
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5. WORKING
FUZZY SETS
1. Represent the degree to which a quality is possessed.
2. The degree of truth is the membership and has value
in the range [0,1]
3. The membership function is a graphical representation
of the magnitude of participation of each input
4. Example: Temp { Freezing/Very Cold, Cold, Warm,
Hot}
Thus the coldness at 55 F
is 0.7 and warmness is
0.3. Thus it is 70% cool
and 30% warm 50 70 90 1103010
Temp. (F°)
Freezing Cool Warm Hot
0
1
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6. WORKING
RULES
1. usually uses IF-THEN rules
2. Rules expressed in the form:
IF variable IS property THEN action
3. Example of Temperature regulator
IF temperature IS very cold THEN stop fan
IF temperature IS cold THEN turn down fan
IF temperature IS normal THEN maintain level
IF temperature IS hot THEN speed up fan
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7. WORKING
How do we use fuzzy membership functions in
predicate logic?
Fuzzy logic Connectives for 2 fuzzy variables:
Fuzzy Conjunction, (AND Operator)
Fuzzy Disjunction, (OR operator)
Fuzzy Conjunction
AB = min(A, B)
Fuzzy Disjunction
AB = max(A, B)
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8. WHY USE FUZZY LOGIC?
Inherently robust since no precise inputs
required
Can be programmed to fail safely if a
feedback sensor stops working
Can be modified easily to improve or alter
system performance
inexpensive sensors can be used thus
keeping the overall system cost and
complexity low.
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9. STEPS IN FUZZY LOGIC CONTROL
Create the membership values
(fuzzification).
Specify the rule table.
Determine procedure for defuzzifying the
result.
EXAMPLE: Speed Calculation
Speed depends upon weather and temperature
How fast to go if it is
65 F° temp. and
25 % Cloud Cover ?
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11. EXAMPLE
RULES
If it's Sunny and Warm, drive Fast
Sunny(Cover)Warm(Temp)Fast(Speed)
0.8 0.7 = 0.7
Fast = 0.7
If it's Cloudy and Cool, drive Slow
Cloudy(Cover)Cool(Temp)Slow(Speed)
0.2 0.3 = 0.2
Slow = 0.2
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12. EXAMPLE
DEFUZZIFICATION
Speed is 20% Slow and 70% Fast
Find location where membership is 100%
Speed = weighted mean
= (0.2*25+0.7*75)/(0.2+0.7)
= 63.8 mph
50 75 100250
Speed (mph)
Slow Fast
0
1
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13. APPLICATIONS
Washing Machines
Anti-Lock Braking System
Anti sway crane control
Flight Control in planes
In Air -Conditioning
Cutting force optimization in machining
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14. APPLICATIONS IN MANUFACTURING
A (FLC) is used to adjust feed rate to
regulate the cutting force of milling processes
in a vertical machining centre
The cutting force and the change in cutting
force over a interval of time is measured and
provided as input.
To achieve high precision contour machining
In Positioning of presses by control of drives
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15. ANTI SWAY CRANE CONTROL
The sway must be reduced to zero for load
release when the target position is reached
Two input variables- Position and sway angle
to control output variable-speed
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The 64 ton crane of Hochtief Corp. uses
fuzzyPLC based anti-sway positioning control
Block Diagram for fuzzy logic crane control system
16. CONCLUSION
Fuzzy Logic provides a completely different,
way to approach a control problem.
Focus on what the system should do rather
than trying to understand how it works.
Leads to quicker, cheaper solutions.
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