Project #3: Collaborative Learning using Fuzzy Logic (CLIFF)

1. An Extension of Fuzzy Collaborative Robotic Pong (FLIP) Project #3: Collaborative Learning using Fuzzy Logic (CLIFF) Sponsored by The National Science Foundation Grant ID No: DUE-0756921 Sophia Mitchell, Pre-Junior, Aerospace Engineering ACCEND College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH Dr. Kelly Cohen, School of Aerospace Systems

2. Outline • Goals & Objectives • Introduction • Fuzzy Logic • Literature Review • Scenario • Methods • Current Progress & Results • Discussion • Future Work • Timeline

3. Overall Objective Mission Control Exploring and exploiting the interactions between humans and intelligent robots to create a synergetic team.

4. Research Goal Develop a robotic coach that learns from its opponent in order to coach its team to a win in the game of PONG. Collaborative robots Human players provide uncertainty. Robotic Coach

5. Research Objective GOOD PLAYER Human or Robotic Team B Robotic Team A Coach a “bad” robotic FLIP team until they beat the “good” team at least 75% of the time Robotic Coach

6. Research Objective

7. Fuzzy Logic • Allows classification of variables for more human-like reasoning. • Common terms • Inputs • Rules • Outputs • Membership Function • Fuzzy Inference System (FIS)

8. Fuzzy Decision Making Bald Not Bald 0 25 50 75 100 Percent of hair on head

9. Type 2 Fuzzy Logic • Brings uncertainty into the membership functions of a fuzzy set • Linguistic uncertainties can be modeled that were not visible in Type 1 fuzzy sets • Allows for more noisy measurements to be quantified

10. Gaussian Singleton Interval Type-2 Fuzzy Inference System (Gauss-INST2-FIS) Equation 1: Variable Gaussian Membership Function Uses a Gaussian primary membership function (μA(x)) Constant mean (m) Variable standard deviation (σ,σ1, σ2)

11. Literature Review • Shown us several things: • Type -2 Fuzzy logic is being (slowly) still developed • No paper could be found so far that has both the idea of a coach and type-2 logic. • Learning many helpful tips with type 2 logic • Benchmark problem resulted from one literature review article • One MATLAB code is published for Type-2 fuzzy logic systems • Example problems from textbook • Spotty topics • Not all types and functions were coded

12. Methods

13. Methods • Chose environment (MATLAB) • Complete the Benchmark Problem • Use MATLAB development to create T-2 Fuzzy players • Create the coach • Develop the team with the coach • Test • Refine • Chose environment (MATLAB) • Complete the Benchmark Problem • Use MATLAB development to create T-2 Fuzzy players • Create the coach • Develop the team with the coach • Test • Refine

14. Benchmark Problem Methods • Model the problem • Solve using type-1 fuzzy logic • Create the type-2 fuzzy logic toolbox in MATLAB • Test the type-2 logic

15. Benchmark Problem

16. The Problem • “Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers” [2] • Filling a drum with water (controls) • Use pump 1 to control water level in tank 2

17. Equations A = Cross-sectional drum area H = Liquid level Q = Volumetric flow rate into the drum α = Discharge coefficients

18. The method • Use the dynamic equations outlined in the research paper • Create the Type 2 functions outlined in the paper • Carefully note changes in result due to changes in m, δand membership function position. • Work with the Type 2 functions to replicate results

19. Why? • Development of Type-2 Fuzzy Logic Software • Needed for work on CLIFF • Increased familiarity • Known results verify the created software • Software will be directly translated into research • Allows added sophistication due to better understanding of the method

20. Results

21. Their Membership Functions - e

22. My Membership Functions - e

23. Their Membership Functions - edot

24. My Membership Functions - edot

26. Results

27. Discussion

28. Discussion • Type two system produces sensible results • Benchmark problem simulator brings up a good point about type 1 logic • Compare best possible solutions

29. Conclusions • Both type-1 and type-2 fuzzy logic are very useful in controls applications • Still not convinced if type-2 is better • Fuzzy logic is a great tool to use for emulating human reasoning • Creating a type-2 fuzzy logic toolbox is very time consuming

30. Future Work • Optimizing type-1 and type-2 results in the benchmark problem • Bringing T-2FIS into FLIP • Change only part of the membership functions to type-2 • Cascading logic using Type-2 • Coach will use Type -2

31. Future Work • Conferences • Undergraduate Research Forum • AIAA Aerospace Sciences Meeting (ASM) 2014

32. Future Plans • Continue research in aerospace engineering • Complete my Bachelors and Masters degrees through the ACCEND program at the University of Cincinnati • Pursue a PhD • NASA - JPL Go to space.

33. Acknowledgements UC AY-REU program Dr. Kelly Cohen MOST-Aerospace Labs

34. References [1] Baklouti, Nesrine, Robert John, and Adel Alimi. "Interval Type-2 Fuzzy Logic Control of Mobile Robots."Journal of Intelligent Learning Systems and Applications. 4.November 2012 (2012): 291-302. Web. 18 Feb. 2013. [2] DongruiWu, Woei Wan Tan, Genetic learning and performance evaluation of interval type-2 fuzzy logic controllers, Engineering Applications of Artificial Intelligence, Volume 19, Issue 8, December 2006, Pages 829-841, ISSN 0952-1976, 10.1016/j.engappai.2005.12.011. (http://www.sciencedirect.com/science/article/pii/S0952197606000388) [3] Mendel, Jerry. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions. Upper Saddle River, NJ: Prentice Hall PTR, 2001. Print. [4] Castillo, Oscar, and Patricia Melin. Type-2 Fuzzy Logic: Theory and Applications. 1. Heidelberg: Springer, 2008. Print. [5] Castillo, Oscar. Type-2 Fuzzy Logic in Intelligent Control Applications. 1. Heidelberg: Springer, 2012. eBook.