Fuzzy Logic in Washing Machine

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.

1. Fuzzy logic in Washing Machine Presented By: M.S.C. I.T SEM 2 02 Khyat Anjaria 09 Deep Chothani 11 Harsh Gor

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. 

3. Washing machine model Reference

4. Block diagram of washing machine

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 traditional methods of control design. We address this design problem using fuzzy logic .

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

8. Fuzzy Logic Toolbox in MATLAB

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.

10. Fuzzy logic Designer

11. Fuzzy Membership Function Editor

12. Fuzzy Membership F unction Editor

13. Fuzzy Rule Editor

14. Fuzzy Rule Viewer

15. Fuzzy Surface viewer

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 .

17. Hardware Used Arduino 16x2 LCD

18. Ultrasonic Sensor DC Motor

19. L298N Motor Controller

20. LCD Circuit Diagram

21. Ultrasonic Sensor Circuit Diagram

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.