Journal of Mechanical Science and Technology

  • 제17권1호
  • /
  • Pages.105-113
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  • 2003
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  • 1738-494X(pISSN)
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  • 1976-3824(eISSN)
대한기계학회 (The Korean Society of Mechanical Engineers)
권호 표지

An LMI-Based Fuzzy State Feedback Control with Multi-objectives

  • Hong, Sung-Kyung (School of Mechanical & Aerospace Engineering, Sejong University) ;
  • Yoonsu Nam (Department of Mechanical Engineering, Kangwon National University)
  • 발행 : 2003.01.01
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초록

This paper proposes a systematic design methodology for the Takagi-Sugeno (TS) model based fuzzy state feedback control system with multi-objectives. In this investigation, the objectives are set to be guaranteed stability and pre-specified transient performance, and this scheme is applied to a nonlinear magnetic bearing system. More significantly, in the proposed methodology, the control design problems that consider both stability and desired transient performance are reduced to the standard LMI problems. Therefore, solving these LMI constraints directly (not trial and error) lead to a fuzzy state-feedback controller such that the resulting fuzzy control system meets the above two objectives. Simulation and experimentation results show that the Proposed LMI-based design methodology yields not only maximized stability boundary but also the desired transient responses.

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참고문헌

  1. Chilali, M. and Gahinet, P., 1996, '$H_{infty}$ design with pole placement constraints: an LMI approach,' IEEE Trans. Automatic Control, Vol. 41, No. 3, pp. 358-367 https://doi.org/10.1109/9.486637
  2. Gahinet, P, Nemirovski, A., Laud, A. and Chilali, M, 1995, The LMI Control Toolbox, The Math Works, Inc., 1995
  3. Hong, S. K. and Langari, R., 1997, 'Experiments of Fuzzy Logic Based Control of a Magnetic Bearing System,' in Proc. North American Fuzzy Information Processing Society (Syracuse, New York), pp. 90-95 https://doi.org/10.1109/NAFIPS.1997.624017
  4. Hong, S. K. and Langari, R., 1999, 'Fuzzy Modeling and Control of a Nonlinear Magnetic Bearing System,' Journal of Intelligent & Fuzzy Systems, Vol. 7, No. 1, pp. 335-346
  5. Hong, S. K. and Langari, R., 2000, 'Robust Fuzzy Control of a Magnetic Bearing System Subject to Harmonic Disturbances,' IEEE Trans. on Control System Technology, Vol. 8, No. 2, pp. 366-371 https://doi.org/10.1109/87.826808
  6. Hong, S. K. and Langari, R., 2000, 'An LMI Based H-infinitive Fuzzy Control System Design with TS Framework,' Information Sciences : An International Journal, Vol. 123, No. 1, pp. 163-179 https://doi.org/10.1016/S0020-0255(99)00131-0
  7. Takagi, T. and Sugeno, M., 1985, 'Fuzzy Identification of Systems and its Application to Modeling and Control,' IEEE Trans. Syst. Man. Cybern., Vol. 15, pp. 116-132 https://doi.org/10.1109/TSMC.1985.6313399
  8. Tanaka, K. and Sugeno, M., 1992, 'Stability Analysis and Design of Fuzzy Control systems,' Fuzzy Sets Syst., Vol 45, No. 2, pp. 135-156 https://doi.org/10.1016/0165-0114(92)90113-I
  9. Tanaka, K. and Sano, M., 1994, 'A Robust Stabilization Problem of Fuzzy Control Systems and its Application to Backing up Control of a Truck-Trailer,' IEEE Trans. on Fuzzy Systems, Vol. 2, No. 2, pp. 119-134 https://doi.org/10.1109/91.277961
  10. Wang, H. O., Tanaka, K. and Griffin, M. F., 1996, 'An Approach to Fuzzy Control of Nonlinear Systems : Stabilty and Design Issues,' IEEE Trans. on Fuzzy Systems, Vol. 4, No. 1, pp. 14-23 https://doi.org/10.1109/91.481841
  11. Zhao, J., Gorez, R. and Wertz, V., 1996, 'Synthesis of Fuzzy Control Systems with Desired Performances,' in Proc. IEEE Int. Symp. Intelligent Control, pp. 115-120, Dearborn, MI https://doi.org/10.1109/ISIC.1996.556187