Understanding Fuzzy Logic in Washing Machine.
How fuzzy logic control washing time based on the user inputs.
Use of Matlab for creating Fuzzy Diagrams.
An Arduino prototype to demonstrate the working of washing machine based on time input by user.
i will provide Arduino code link as soon as possible.
2. INTRODUCTION TO FUZZY LOGIC IN
WASHING MACHINE
• Fuzzy logic washing machines are gaining popularity. These
machines offer the advantages
of performance, productivity, simplicity and less cost. Sensors
continually monitor varying conditions inside the machine and
accordingly adjust operations for the best wash results.
• The fuzzy logic checks for the extent of dirt and grease, the amount
of soap and water to add, direction of spin, and so on. The machine
rebalances washing load to ensure correct spinning. Neuro fuzzy
logic incorporates optical sensors to sense the dirt in water and
a fabric sensor to detect the type of fabric and accordingly adjust
wash cycle.
5. WASH SENSOR
• Assume that we have these inputs at our hand,
• The degree of dirt is determined by the transparency of the wash
water. The dirtier the clothes, less transparent the water being
analyzed by the sensors is.
• Type of dirt determines the quality of dirt. Greasy cloths, for
example, take longer for water transparency to reach transparency
because grease is less soluble in water than other forms of dirt. type
of dirt is determined by the time of saturation.
• Because the input/output relationship is not clear, the design of a
washing machine controller has not in the past lent itself to
6. FUZZY LOGIC APPROACH
• Convert crisp values in Fuzzy values
• Rule Evaluation (Rule Application)
• Use membership functions
• Fuzzification
• Defuzzification (Obtaining crisp or actual results)
7. FUZZY INFERENCE SYSTEM
• Fuzzy Inference System (FIS) is a way of mapping and input space to
an output space using fuzzy logic
• The rules in FIS are fuzzy production rules like
• if (antedecent) then (consequence)
• If x is low and y is high then z is medium
• Set of rules in a FIS is known as knowledge base
9. • Using MATLAB’s Fuzzy Logic Toolbox we can create and edit
fuzzy inference systems with Fuzzy Logic Toolbox Software.
• Simulink Software helps us to test our fuzzy system in a block
diagram simulation environment.
• It can work as a stand-alone fuzzy inference engine.
16. SCIKIT-FUZZY [THE PROGRAMMING PART]
• A fuzzy logic toolbox for SciPy.
• Collection of fuzzy logic algorithms written In
Python.
• Despite some of the lengthy rule sets, Scikit-
Fuzzy’s control system can execute and finish
calculations in miliseconds.
22. 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.