1. Project #3: Collaborative Learning using Fuzzy Logic (CLIFF) 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 An Extension of Fuzzy Collaborative Robotic Pong (FLIP) Sponsored by The National Science Foundation Grant ID No: DUE-0756921 1

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

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

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. Human players provide uncertainty. Collaborative robots 4 Robotic Coach

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

6. Research Objective FLIP team plays the game Score! Coach analysis Coach decides changes Coach applies changes to the FLIP team 6

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

8. Fuzzy Decision Making 8 BaldNot Bald Percent of hair on head 0 25 50 75 100

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 9

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

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 11

12. METHODS 12

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 13 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 14

15. BENCHMARK PROBLEM 15

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 16

17. Equations 17 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, δ and membership function position. Work with the Type 2 functions to replicate results 18

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 19

20. RESULTS 20

21. Their Membership Functions - e 21

22. My Membership Functions - e 22

23. Their Membership Functions - edot 23

24. My Membership Functions - edot 24

25. 25

26. Results 26

27. DISCUSSION 27

28. Discussion 28 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 29

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 30

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

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. 32

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

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] Dongrui Wu, 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)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. 34