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Design of T-S Fuzzy Model based Adaptive Fuzzy Observer and Controller

  • Ahn, Chang-Hwan (Department of Digital Electronics, Inha Technical College)
  • 발행 : 2009.11.30

초록

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

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참고문헌

  1. C.-S. CHEN and W.-L. CHEN, Robust model reference adaptive control of nonlinear systems using fuzzy systems, Int. J. Syst Sci., vol.27, no.12, pp.1435-1442, 1996 https://doi.org/10.1080/00207729608929349
  2. Chang-Ho-Hyun, Chang-Woo Park and Mignon Park, A robust indirect adaptive fuzzy state feedback regulator based on Takagi-Sugeno fuzzy model, J. Korea Fuzzy Logic and Intelligent Systems Society, vol.12, no.6, pp.554-558, October 2002
  3. Chi-Tsong Chen, Linear system theory and design 3rd ed., The Oxford series in electrical and computer engineering, 1998
  4. E. TZIRKEL -HANCOCK and F. FALL SIDE, Stable control of nonlinear systems using neural networks, Rebust Nonlinear Control, vol.2, pp.63-68, 1992 https://doi.org/10.1002/rnc.4590020105
  5. Euntai Kim, A fuzzy disturbance observer and its application to control, IEEE Trans. Fuzzy Syst, vol10, no.1, pp.77-84, February 2002 https://doi.org/10.1109/91.983280
  6. J.-H. Park, and G. T. PARK, Robust adaptive fuzzy controller for nonlinear system with unknown nonlinearities, Int. J. Intell. Fuzzy Syst., vol.10, no.2, pp.87-98, 2000
  7. J. -J. E. Slotine and W. Li, Applied nonlinear control, Englewood Cliffs, NJ: Prentice-Hall, 1991
  8. Kazuo Tanaka, Takayuki Ikeda and Hua O. Wang, Fuzzy regulators and fuzzy observers: Relaxed stability conditions and LMI-based designs, IEEE Trans. Fuzzy syst., vol.6, no.2, pp.250-265, May 1998 https://doi.org/10.1109/91.669023
  9. L.-X. Wang, A Course in Fuzzy systems and Control, Prentice-Hall International, Inc., 1997
  10. L. X Wang, Stable adaptive fuzzy controllers with application to inverted pendulum tracking, IEEE Trans. Fuzzy Syst., vol.26, no.5, pp.677-691, 1996
  11. L. X. WANG, Stable adaptive fuzzy controllers with application to inverted pendulum tracking, IEEE Trans. Fuzzy Syst., vol.26, no.5, pp.677-691, Oct. 1996
  12. M. Sugeno and G. T. Kang, Structure identification of fuzzy model, fuzzy Sets and Systems, vol.28, pp.15-33, 1988 https://doi.org/10.1016/0165-0114(88)90113-3
  13. M. U. POLYCARPOU and M. J. MEARS, Stable adaptive tracking of uncertain systems using nonlinearly parameterized on-line approxirrators, Int. J. Control, vol.70, no.3, pp.363-384, 1998 https://doi.org/10.1080/002071798222280
  14. Moez Feki, An adaptive chaos synchronization scheme applied to secure communication, Chaos, Solitons and Fractals, vol.18, issue 1, pp.141-148, September 2003 https://doi.org/10.1016/S0960-0779(02)00585-4
  15. S. FABRI and V. KADIRKAMANATHAN, Dynamic structure neural networks for stable adaptive control of nonlinear system, IEEE Trans. neural Netw., vol.7, no.5, pp.1151-1167, 1996 https://doi.org/10.1109/72.536311
  16. S. S. GE, C. C. HANG, and T. ZHANG, Adaptive neural network control of nonlinear systems by state and output feedback, IEEE Trans. Syst., Man Cybern. B. Cybrern., vol.29, no.6, pp.818-828, 1999 https://doi.org/10.1109/3477.809035
  17. T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Systems, Man and Cybernetics, vol.15, no.1, pp.116-132, 1985
  18. Xiao-Jun Ma, Zeng-Qi Sun and Yan-Yan He, Analysis and design of fuzzy controller and fuzzy observer, IEEE Trans. Fuzzy syst., vol.6, no. 1 , pp.41-51, February 1998 https://doi.org/10.1109/91.660807