International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
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On the Design of Simple-structured Adaptive Fuzzy Logic Controllers

  • Park, Byung-Jae (School of Computer and Communication Eng., Daegu University) ;
  • Kwak, Seong-Woo (Department of Electrical Eng., Keimyung University)
  • Published : 2003.06.01
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Abstract

One of the methods to simplify the design process for a fuzzy logic controller (FLC) is to reduce the number of variables representing the rule antecedent. This in turn decreases the number of control rules, membership functions, and scaling factors. For this purpose, we designed a single-input FLC that uses a sole fuzzy input variable. However, it is still deficient in the capability of adapting some varying operating conditions although it provides a simple method for the design of FLC's. We here design two simple-structured adaptive fuzzy logic controllers (SAFLC's) using the concept of the single-input FLC. Linguistic fuzzy control rules are directly incorporated into the controller by a fuzzy basis function. Thus some parameters of the membership functions characterizing the linguistic terms of the fuzzy control rules can be adjusted by an adaptive law. In our controllers, center values of fuzzy sets are directly adjusted by an adaptive law. Two SAFLC's are designed. One of them uses a Hurwitz error dynamics and the other a switching function of the sliding mode control (SMC). We also prove that 1） their closed-loop systems are globally stable in the sense that all signals involved are bounded and 2） their tracking errors converge to zero asymptotically. We perform computer simulations using a nonlinear plant.

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References

  1. D. Driankov, H. Hellendoorn, and M. Rainfrank, AnIntroduction to Fuzzy ControI. Springer- Verlag Berlin Heidelberg, 1993
  2. B.-J. Choi, S.-W. Kwak and B. K. Kim, 'Design and Stability Analysis of Single-Input Fuzzy Logic Controller,' IEEE Trans. Syst., Man, Cybern., vol. 30, no. 2, pp. 303-309, 2000 https://doi.org/10.1109/3477.836378
  3. L. X. Wang, 'Stable Adaptive Fuzzy Control ofNonlinear Systems,' IEEE Trans. Fuzzy Syst., vol. 1, no.2, pp. 146-154, 1993 https://doi.org/10.1109/91.227383
  4. C.-Y. Su and Y. Stepanenko, 'Adaptive control of aclass of nonlinear systems with fuzzy logic,' IEEE Trans. Fuzzy Syst., vol. 2, no. 4, pp. 285-294, 1994 https://doi.org/10.1109/91.324808
  5. C.-Y. Su and Y. Stepanenko, 'Adaptive control of aclass of nonlinear systems with fuzzy logic,' IEEE Trans. Fuzzy Syst., vol. 2, no. 4, PP. 285-294, 1994 https://doi.org/10.1109/91.324808
  6. N. Colea, A. Golea, and M. Kadjoudj, 'Indirect fuzzyadaptive control: hyperstability approach,' IEEE 28th AnnuaI Conf. of the Industrial Etectronics Society, vol.2, pp. 1487-1492, 2002
  7. Gang Feng, 'An approach to adaptive control of fuzzydynamic systems ,' IEEE Trans. Fuzzy Syst., vol. 10,no. 2, pp. 268-275, 2002 https://doi.org/10.1109/91.995127
  8. H.-X. Li and S. Tong, 'A hybrid adaptive fuzzy controlfor a class of nonlinear MIMO systems,' IEEE Trans. Fuzzy Syst., vol. 11, no. 1, PP. 24-34, 2003 https://doi.org/10.1109/TFUZZ.2002.806314
  9. N. Golea, A. Golea, and K.. Benmahammed, 'Fuzzymodel reference adaptive control,' JEEE Trans. Fuzzy Syst., vol. 10, no. 4, pp. 436-444, 2002 https://doi.org/10.1109/TFUZZ.2002.800694
  10. L.-X. Wang, Adaptive Fuzzy Systems and ControI,Prentice-Hall International, Inc., New Jersey, 1994
  11. H. K. Khalil, Nontinear Systems, Macmillan Publishing Co., New York, 1996
  12. K. M. Passino and S. Yorkovich, Fuzzy Controt, Addison Wesley Longman, Menlo Park, CA, 1998

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