PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

A new robust adaptive control of a class of MIMO nonlinear systems with fuzzy approximators

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Based on the Lyapunov synthesis approach, several adaptive fuzzy control schemes have been developed during the last few years. In this paper we develop a robust adaptive fuzzy control law for MIMO nonlinear system class. The proposed method uses the Sugeno-Takagi fuzzy system as an universal approximator of continuous nonlinear functions. The adaptive controllaw is established based on the Lyapunov method. So, the output convergence, the boundedness of the parameters and the ststes are derived. Moreover, the fuzzy adaptive law incorporates a compensatory sliding term, which compensates for effects of the unavoidable reconstruction errors.
Rocznik
Strony
341--358
Opis fizyczny
Bibliogr. 32 poz., rys., tab.
Twórcy
autor
autor
  • Laboratoire de Commande des Processus, Ecole Nationale polytechnique B. P. 182, El-Harrach, Algers, Algeria, h_tlemcani@yahoo.fr
Bibliografia
  • [1] L. A. ZADEH: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Systems Man Cybernet, 3 (1973), 28-44.
  • [2] E. H. MAMDANI and S. A SSILIAN: Application of fuzzy algorithms for simple dynamic plant. IEE Proc. Part-D, 121 (1974), 1585-1588.
  • [3] M. SUGENO: Industrial applications of fuzzy control. Elsevier Science Publishers, New York, 1985.
  • [4] S. G. CAO, N. W. REES and G. FENG: Analysis and design for a class of complex cntrol systems. Part I: Fuzzy modelling and identification. Automatica, 33 (1997), 1017-1028.
  • [5] G. FENG, S. G. CAO, N. W. REES and C. K. CHAK: Design of fuzzy control systems with guaranteed stability. Fuzzy Sets Syst., 85 (1997), 1-10.
  • [6] M. C. M. TEIXEIRA and S. H. ZAK: Stabilizing controller design for uncertain nonlinear systems using fuzzy models. IEEE Trans. Fuzzy Syst., 7 (1999), 133-142.
  • [7] S. G. CAO, N. W. REES and G. FENG: Analysis and design of fuzzy control systems using dynamic fuzzy state space models. IEEE Trans. Fuzzy Syst, 7 (1999), 192-200.
  • [8] L. X. WANG and J. M. MENDEL: Fuzzy basis functions, universal approximation and orthogonal least squares learning. IEEE Trans. Neural Networks, 3 (1992), 807-814.
  • [9] L. X. WANG: Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. On Fuzzy Systems, 1 (1993), 146-155.
  • [10] H. LEE and M. TOMIZUKA: Robust adaptive control using a universal approxima-for for SISO nonlinear systems. IEEE Trans. Fuzzy Systems, 8(1), (2000), 95-106.
  • [11] C. Y. CHANG: Adaptive fuzzy based tracking control for nonlinear SISO systems via VSS and H approaches. IEEE Trans. Fuzzy Systems, 9(2001), 278-292.
  • [12] M. HOJATI and S. GAZOR: Hybrid adaptive fuzzy identification and control of 1418. nonlinear systems. IEEE Trans. Fuzzy Systems, 10(2), (2002), 198-210.
  • [13] S. LABIOD and M. S. BOUCHERIT: Direct stable fuzzy adaptive control of a class of SISO nonlinear systems. Archiver of Control Sciences, 13(1), (2003), 95-110.
  • [14] Y. LI, X. ZHUANG, S. QIANG, G. LIU and X. MEI: Stable fuzzy adaptive control for a class of nonlinear systems. Proc. 4th World Congress on Intelligent Control and Automation, China, (2002).
  • [15] N. GOLEA, A. GOLEA and K. BENMAHAMMED: Stable indirect fuzzy adaptive control. Fuzzy Sets and Systems, 137 (2003), 353-366.
  • [16] R. ORDONEZ and K. M. PASSINO: Stable multi-input multi-output adaptive fuzzy/neural control. IEEE Trans. Fuzzy Systems, 7(3), (1999), 345-353.
  • [17] C. Y. CHANG: Robust tracking control for nonlinear MIMO systems via fuzzy approaches. Automatica, 36 (2000), 1535-1545.
  • [18] H. X. LI and S. TONG: A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems. IEEE Trans. Fuzzy Systems, 11(1), (2003), 24-34.
  • [19] H. CHEKIREB, M. TADJINE and D. BOUCHAFFRA: Direct adaptive fuzzy control of nonlinear system class with application. Control and Intelligent Systems, 31(2), (2003), 113-121.
  • [20] J. M. MENDEL: Fuzzy systems for engineering: A tutorial. Proc. IEEE, 83(3), (1995), 347-377.
  • [21] J. S. R. JANG and C. T. SUN: Neuro-fuzzy modeling and control. Proc. of IEEE, 83(3), (1995), 378-406.
  • [22] H. BÜHER: Le reglage par logique floue. Presses Polytechniques Romandes, 1994.
  • [23] M. SUGENO and G. T. KANG: Structure identification of fuzzy model. Fuzzy Sets and Systems, 28 (1988), 15-33.
  • [24] F. H. F. LEUNG, L. K. WONG and P. K. S. TAM: Fuzzy model controller for inverted pendulum. Electronics Letters, 32(2), (1996), 1683-1685.
  • [25] B. KOSKO: Neural networks and fuzzy systems: A dynamical systems approach. Englewood Cliffs, Prentice Hall, 1992.
  • [26] J. E. SLOTINE and W. LI: Applied nonlinear control. Englewood Cliffs, Prentice Hall, 1991.
  • [27] L. X. WANG and J. M. MENDEL: Back-propagation fuzzy systems as nonlinear dynamic system identifiers. Proc. IEEE Mt. Conf on Fuzzy Systems, (1992), 1409-1418.
  • [28] L. WANG: Design and analysis of fuzzy identifiers of nonlinear dynamic systems. IEEE Trans. on Automatic Control, 40(1), (1995), 11-23.
  • [29] F. J. LIN: A permanent-magnet synchronous motor servo drive using selfconstructing fuzzy neural network controller. IEEE Trans. on e Energy Conversion, 19(1), (2004), 66-72.
  • [30] J. T. SPOONER and K. M. PASSINO: Stable adaptive control using fuzzy systems and neural networks. IEEE Trans. Fuzzy Systems, 4 (1996), 339-359.
  • [31] Y. TANG, N. ZHANG and Y. LI: Stable fuzzy adaptive control for a class of nonlinear systems. Fuzzy Sets and Systems, 101 (1999), 31-39.
  • [32] L. X. WANG: Fuzzy systems and control: Design and stability analysis, Prentice-Hall Inc., Englewood Cliffs, 1994.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW3-0028-0007
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.