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

An Extension of Fuzzy Collaborative Robotic Pong (FLIP) 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 1

Outline

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

Overall Objective

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

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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

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Research Objective

Robotic Team A

GOOD PLAYER

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

Research Objective

FLIP team plays the game Coach applies changes to the FLIP team Score!

Coach decides changes Coach analysis 6

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Fuzzy Logic

Allows classification of variables for more human-like reasoning.

• • • • • Common terms Inputs Rules Outputs Membership Function Fuzzy Inference System (FIS) 7

Fuzzy Decision Making

Bald Not Bald

0 25 50 75 100 Percent of hair on head 8

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

Gaussian Singleton Interval Type-2 Fuzzy Inference System (Gauss-INST2-FIS) • • • 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 10

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

METHODS

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Methods

• • • • • • • • • • • • • • Use MATLAB development to create T-2 Fuzzy players 13

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

BENCHMARK PROBLEM

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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

Equations

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

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

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

RESULTS

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Their Membership Functions - e

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My Membership Functions - e

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Their Membership Functions - edot

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My Membership Functions - edot

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Results

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DISCUSSION

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Discussion

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

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

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

Future Work

• Conferences – Undergraduate Research Forum – AIAA Aerospace Sciences Meeting (ASM) 2014 31

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.

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Acknowledgements

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

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 ) [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.

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