Seminar on use of fuzzy system in washing machine with its fuzzification and defuzzification.

1. FUZZY LOGIC CONTROL OF
WASHING MACHINES
PRESENTED BY :
2013BCS003 GHATAGE PALLAVI N. 2013BCS004 KASAR NISHA D.
2013BCS012 ADAKE SUPRIYA S. 2013BCS020 PATIL PRADNYA V.

2. Washing Machine
 Definition of Washing Machine
 Types of Washing Machine
 Top-loaded
 Front loaded
 Classification of Washing machine
 Fully Automatic
 Semi automatic

4. Architecture of Washing Machine
 Water inlet control valve
 Water pump
 Tube (washer drum)
 Door safety sensor
 Detergent drawer
 Drain pipe

5. Optical Sensor
 Light rays to Electronic signals
 detecting a light permeability
 Working of wash sensor
 Fuzzy in Washing Machine

6. Fuzzy Inference(Expert)
System
o Fuzzy Inference System (FIS) is a way of mapping an input
space to an output space using fuzzy logic
o The rules in FIS are fuzzy production rules like
If(antecedent) then (consequence)
if x is low and y is high then z is medium
o Set of rules in a FIS is known as knowledge base

8. Mamdani fuzzy inference
oFunctional operations in FIS
−Fuzzification
−Fuzzy Inferencing
−Aggregation
−Defuzzification

9. Sugeno fuzzy inference
o Similar to the Mamdani method changed
only a rule consequent. The format of the
Sugeno -style fuzzy rule is
IF x is A
AND y is B
THEN z is f (x, y)

10. Fuzzy Controller

11. Fuzzification
Convert Crisp value in fuzzy value
Use membership function
Fuzzifier

12. Membership Functions of
input

13. Membership function of output

14. Fuzzy Rules
Type of Dirtiness
Dirtiness

15. Defuzzification
 The last step in the fuzzy inference process is
defuzzification. Fuzziness helps us to evaluate the
rules, but the final output of a fuzzy system has to
be a crisp number. The input for the
defuzzification process is the aggregate output
fuzzy set and the output is a single number.
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16. Defuzzifier
 Converts the fuzzy output of the inference engine to crisp using
membership functions analogous to the ones used by the fuzzifier.
 Five commonly used defuzzifying methods:
 Centroid of area (COA)
 Bisector of area (BOA)
 Mean of maximum (MOM)
 Smallest of maximum (SOM)
 Largest of maximum (LOM)

17. Defuzzifier

18. Defuzzifier
( )
,
( )
A
Z
COA
A
Z
z zdz
z
z dz





( ) ( ) ,
BOA
BOA
z
A A
z
z dz z dz


  
*
,
{ ; ( ) }
Z
MOM
Z
A
zdz
z
dz
where Z z z 



  



19. Example
R1 : If X is small then Y is small
R2 : If X is medium then Y is medium
R3 : If X is large then Y is large
X = input  [10, 10]
Y = output  [0, 10]
Overall input-output curve
Max-min composition and centroid defuzzification were used.

20. Example
R1: If X is small & Y is small then Z is negative large
R2: If X is small & Y is large then Z is negative small
R3: If X is large & Y is small then Z is positive small
R4: If X is large & Y is large then Z is positive large
X, Y, Z  [5, 5]
Overall input-output curve
Max-min composition and centroid defuzzification were used.

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 Centroid defuzzification method finds a point
representing the centre of gravity of the fuzzy set, A,
on the interval, ab.
 A reasonable estimate can be obtained by calculating
it over a sample of points.
1.0
0.0
0.2
0.4
0.6
0.8
160 170 180 190 200
a b
210
A
150
X

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Centre of gravity (COG):
4.67
5.05.05.05.02.02.02.02.01.01.01.0
5.0)100908070(2.0)60504030(1.0)20100(



COG
1.0
0.0
0.2
0.4
0.6
0.8
0 20 30 40 5010 70 80 90 10060
Z
Degreeof
Membership
67.4

23. Conclusion
 By the use of fuzzy logic control we have been able to obtain a wash time for
different type of dirt and different degree of dirt.
 The conventional method required the human interruption to decide upon what
should be the wash time for different cloths.
 The situation analysis ability has been incorporated in the machine which makes
the machine much more automatic and represents the decision taking power of
the new arrangement.
 The strength of fuzzy logic is that we are able to model words by the use of fuzzy
sets.

24. Future Work
 1. A more fully automatic washing machine is straightforward to design using
fuzzy logic technology.
 2. Increasing the controller work that controls only the wash time of a washing
machine, to design process can be extended to other control variables such as
water level and spin speed. The formulation and implementation of membership
functions and rules is similar to that shown for wash time.
 3. If the Optical sensor is available in the future, the hardware also will be
available to construct it in the real world.
 4. Full "Fuzzy Logic“ automatic control system, includes the correct temperature,
washing time, and washing speed.

25. Thank YOU!!!!