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Fuzzy Model Reference Learning Control

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Advances in Fuzzy Control

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 16))

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Abstract

Over recent years, fuzzy control has emerged as a practical alternative to classical control schemes when one is interested in controlling certain time-varying, non-linear, and ill-defined processes. There have in fact been several successful commercial and industrial applications of fuzzy control [1] — [5]. Despite this success, there exist several significant drawbacks of this approach:

  1. 1.

    The design of fuzzy controllers is usually performed in an ad hoc manner; hence, it is often not clear exactly how to justify the choices for many parameters in the fuzzy controller (e.g., the membership functions, defuzzification strategy, and fuzzy inference strategy).

  2. 2.

    The fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations, structural changes, or environmental disturbances occur.

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References

  1. W. Kickert and H. V. N. Lemke, “Application of a fuzzy controller in a warm water plant,” Automatica, vol. 12, no. 4, pp. 301–308, 1976.

    Article  Google Scholar 

  2. E. Mamdani and S. Assilian, “An experiment in linguistic synthesis with a fuzzy logic controller,” Intl. Journal of Man-Machine Studies, vol. 7, no. 1, pp. 1–13, 1975.

    Article  MATH  Google Scholar 

  3. J. Bernard, “Use of a rule-based system for process control,” IEEE Control Systems Magazine, vol. 8, pp. 3–13, October 1988.

    Article  Google Scholar 

  4. Y. Li and C. Lau, “Development of fuzzy algorithms for servo systems,” IEEE Control Systems Magazine, vol. 9, pp. 65–72, April 1989.

    Article  Google Scholar 

  5. K. Self, “Designing with fuzzy logic,” IEEE Spectrum, pp. 42–105 and 105, November 1990.

    Google Scholar 

  6. T. Procyk and E. Mamdani, “A linguistic self-organizing process controller,” Automatica, vol. 15, no. 1, pp. 15–30, 1979.

    Article  MATH  Google Scholar 

  7. E. Scharf and N. Mandic, “The application of a fuzzy controller to the control of a multi-degree-of-freedom robot arm,” in Industrial Applications of Fuzzy Control, pp. 41–62, Amsterdam, the Netherlands: M. Sugeno (ed.), 1985.

    Google Scholar 

  8. R. Tanscheit and E. Scharf, “Experiments with the use of a rule-based self-organising controller for robotics applications,” Fuzzy Sets and Systems, vol. 26, pp. 195–214, 1988.

    Article  Google Scholar 

  9. S. Shao, “Fuzzy self-organizing controller and its application for dynamic processes,” Fuzzy Sets and Systems, vol. 26, pp. 151–164, 1988.

    Article  MathSciNet  Google Scholar 

  10. S. Isaka, A. Sebald, A. Karími, N. Smith, and M. Quinn, “On the design and performance evaluation of adaptive fuzzy controllers,” Proceedings, 1988 IEEE Conference on Decision and Control, pp. 1068–1069, Austin, Texas, December 1988.

    Google Scholar 

  11. S. Daley and K. F. Gill, “Comparison of a fuzzy logic controller with a P+D control law,” Journal of Dynamical System, Measurement, and Control, vol. 111, pp. 128–137, June 1989.

    Article  MATH  Google Scholar 

  12. S. Daley and K. F. Gill, “Altitude control of a spacecraft using an extended self-organizing fuzzy logic controller,” Proc. I. Mech. E., vol. 201, no. 2, pp. 97–106, 1987.

    Article  Google Scholar 

  13. S. Daley and K. F. Gill, “A design study of a self-organizing fuzzy logic controller,” Proc. L Mech. E., vol. 200, pp. 59–69, 1986.

    Article  Google Scholar 

  14. K. Åström and B. Wittenmark, Adaptive Control. Reading, Massachusetts: Addison-Wesley Publishing Company, 1989.

    MATH  Google Scholar 

  15. K. Narendra and A. Annaswamy, Stable Adaptive Systems. Englewood Cliffs, New Jersey: Prentice Hall, 1989.

    MATH  Google Scholar 

  16. T. Yamazaki, An improved algorithm for a self-organizing controller and its experimental analysis. PhD thesis, London University, 1982.

    Google Scholar 

  17. J. Layne and K. Passino, “Fuzzy model reference learning control,” Proceedings of the 1st IEEE Conference on Control Applications, pp. 686–691, Dayton, Ohio, September 1992.

    Google Scholar 

  18. J. Layne, “Fuzzy model reference learning control,” Master’s thesis, Department of Electrical Engineering, The Ohio State University, March 3 1992.

    Google Scholar 

  19. G. Bartolini, G. Casalino, F. Davoli, R. M. M. Mastretta, and E. Morten, “Development of performance adaptive fuzzy controllers with applications to continuous casting plants,” Industrial Application of Fuzzy Control, pp. 73–86, 1985.

    Google Scholar 

  20. H. Takahashi, “Automatic speed control device using self-tuning fuzzy logic,” 1988 IEEE Workshop on Automotive Applications of Electronics, pp. 65–71, Dearborn, Michigan, October 1988.

    Google Scholar 

  21. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on systems, Man, and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985.

    Article  MATH  Google Scholar 

  22. L. Wang and J. Mendel, “Generating fuzzy rules by learning from examples,” Proceedings, 1991 IEEE International Symposium on Intelligent Control, pp. 263–268, Arlington, Virginia, August 1991.

    Google Scholar 

  23. R. M. Tong, “Some properties of fuzzy feedback systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 10, pp. 327–330, June 1980.

    Article  MATH  Google Scholar 

  24. A. Cumani, “On a possibilistic approach to the analysis of fuzzy feedback systems,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 12, pp. 417–422, May/June 1982.

    Article  MathSciNet  MATH  Google Scholar 

  25. E. Czogala and W. Pedrycz, “On identification in fuzzy systems and its applications in control problems,” Fuzzy Sets and Systems, vol. 6, pp. 73–83, 1981.

    Article  MathSciNet  MATH  Google Scholar 

  26. E. Czogala and W. Pedrycz, “Control problems in fuzzy systems,” Fuzzy Sets and Systems, vol. 7, pp. 257–273, 1982.

    Article  MathSciNet  MATH  Google Scholar 

  27. C. Batur, A. Srinivasan, and C. Chan, “Automatic rule based model generation for uncertain complex dynamical systems,” Proceedings, 1991 IEEE International Symposium on Intelligent Control, pp. 275–279, 1991.

    Google Scholar 

  28. F. V. D. Rhee, H. V. N. Lemke, and J. Dijkman, “Knowledge based fuzzy control of systems,” IEEE Transactions on Automatic Control, vol. 35, pp. 148–155, February 1990.

    Article  MATH  Google Scholar 

  29. P. Graham and R. Newell, “Fuzzy adaptive control of a first-order process,” Fuzzy Sets and Systems, vol. 31, pp. 47–65, 1989.

    Article  MathSciNet  MATH  Google Scholar 

  30. P. Graham and R. Newell, “Fuzzy identification and control of a liquid level rig,” Fuzzy Sets and Systems, vol. 8, pp. 255–273, 1988.

    Article  Google Scholar 

  31. C. Lee, “Fuzzy logic in control systems: Fuzzy logic controller-part I,” IEEE Trans, on Systems, Man. and Cybernetics, vol. 20, pp. 404–418, March/April 1990.

    Article  MATH  Google Scholar 

  32. J. Farrell and W. Baker, “Learning control systems,” in An Introduction to Intelligent and Autonomous Control Systems (P. Antsaklis and K. Passino, eds.), Kluwer Academic Publishers; Norwell MA, 1993.

    Google Scholar 

  33. J. Layne and K. Passino, “Fuzzy model reference learning control,” Journal of Intelligent and Fuzzy Systems, vol. 4, pp. 33–47, 1996.

    Google Scholar 

  34. J. Layne and K. Passino, “Fuzzy model reference learning control for cargo ship steering,” IEEE Control Systems, vol. 13, no. 6, pp. 23–34, 1993.

    Article  Google Scholar 

  35. J. Layne, K. Passino, and S. Yurkovich, “Fuzzy learning control for anti-skid braking systems,” IEEE Trans. on Control System Technology, vol. 1, pp. 122–129, June 1993.

    Article  Google Scholar 

  36. J. Slotine and W. Li, Applied Nonlinear Control. Englewood Cliffs, New Jersey: Prentice Hall, 1991.

    MATH  Google Scholar 

  37. J. Layne, K. Passino, and S. Yurkovich, “Fuzzy learning control for anti-skid braking systems,” Proc. IEEE Conf. on Decision and Control, pp. 2523–2528, Tucson, AZ, December 1992.

    Google Scholar 

  38. V. Moudgal, W. Kwong, and K. Passino, “Learning control for a two-link flexible mechanism,” Proc. of the American Control Conference, pp. Baltimore, MD, June 1994.

    Google Scholar 

  39. V. Moudgal, W. Kwong, K. Passino, and S. Yurkovich, “Fuzzy learning control for a flexible-link robot,” IEEE Transactions on Fuzzy Systems, vol. 3, no. 2, pp. 199–210, 1995.

    Article  Google Scholar 

  40. W. Kwong and K. Passino, “Fuzzy learning systems for aircraft control law reconfiguration,” Proceedings of the IEEE Int. Symp. on Intelligent Control, pp. Columbus, Ohio, Aug. 16–18 1994.

    Google Scholar 

  41. W. Kwong, K. Passino, E. Laukonen, and S. Yourkovich, “Expert supervision of fuzzy learning systems for fault tolerant aircraft control,” Proceedings of the IEEE, vol. 83, no. 3, pp. 466–483, 1995.

    Article  Google Scholar 

  42. Y. Tang and L. Xu, “Fuzzy logic application for intelligent control of a variable speed drive,” IEEE Transactions on Energy Conversion, pp. 679–685, 1994.

    Google Scholar 

  43. L. Zhen and L. Xu, “A comparison study of three fuzzy schemes for indirect vector control of induction machines,” Preceedings, IEEE Industrial Application Society Annual Meeting, pp. 1725–1732, 1996.

    Google Scholar 

  44. L. Zhen and L. Xu, “Fuzzy learning enhanced speed control of an indirect field oriented induction machine drive,” Preceedings, IEEE International Symposium on Intelligent Control, pp. 109–114, 1996.

    Google Scholar 

  45. J. Zumberge and K. Passino, “A case study in intelligent vs. conventional control for a process control experiment,” Proceedings of the 1996 IEEE International Symposium on Intelligent Control, pp. 37–42, 1996.

    Google Scholar 

  46. W. Lennon and K. Passino, “Intelligent control for brake systems,” Proceedings of the 1995 IEEE International Symposium on Intelligent Control, pp. 499–504, 1995.

    Google Scholar 

  47. W. Kwong and K. Passino, “Dynamically focused fuzzy learning control,” IEEE Transactions on Systems, Man, and Cybernetics — Part B, vol. 26, no. 1, pp. 53–74, 1996.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. WL/AACF, 2185 Avionics Circle, Wright Patterson AFB, OH, 45433-7301, USA

    J. R. Layne

  2. Dept. of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH, 43102, USA

    K. M. Passino

Authors
  1. J. R. Layne
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  2. K. M. Passino
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Linköping, S-58183, Linköping, Sweden

    Dimiter Driankov

  2. Corporate Technology Information and Communications, Dept. ZT IK4, Siemens AG, D-81730, Munich, Germany

    Rainer Palm

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© 1998 Springer-Verlag Berlin Heidelberg

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Layne, J.R., Passino, K.M. (1998). Fuzzy Model Reference Learning Control. In: Driankov, D., Palm, R. (eds) Advances in Fuzzy Control. Studies in Fuzziness and Soft Computing, vol 16. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1886-4_10

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  • DOI: https://doi.org/10.1007/978-3-7908-1886-4_10

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-11053-9

  • Online ISBN: 978-3-7908-1886-4

  • eBook Packages: Springer Book Archive

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