Fuzzy adaptive control of a class of MISO nonlinear systems

• Autorzy: Lagrat I. , Ouakka H. , Boutnhidi I.
• Języki publikacji: EN

Abstrakty

EN

This paper presents a fuzzy adaptive control of a class of MISO nonlinear systems. The dynamic behaviour of each MISO systems is composed of a nonlinear term, interactions effect between the inputs, and disturbances. In these circumstances, adaptive control becomes very difficult to implement and not always an evident task. Thus, the MISO system is approximated by the Takagi-Sugeno fuzzy model. The advantage of this approximation is beneficial in the sense that it allows for converting the nonlinear problem into a linear one. In this respect, the coupling, nonlinearity and unmodeled dynamics are easily compensated. The identification and the control are conducted at the level of each local linear model based on fuzzy approach. The computational load and the complexity of nonlinear approach are reduced and permit wide applicability. The validity and the performance are tested numerically.

Słowa kluczowe

EN

MISO systems; identification; fuzzy clustering; Takagi-Sugeno fuzzy model; adaptive control; nonlinear system

PL

identyfikacja; sterowanie adaptacyjne; układ nieliniowy

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2008
• Tom: Vol. 37, no 1
• Strony: 177--190
• Opis fizyczny: Bibliogr. 22 poz., wykr.

Twórcy

autor

Lagrat I.

autor

Ouakka H.

autor

Boutnhidi I.

• LESSI, Faculte Des Sciences, B. P 1796 Atlas, 30000 Fez, Morocco,

Bibliografia

• BABUSKA, R. (1998) Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston.
• BABUSKA, R., and VEBRUGGEN, H.B. (1995) Identification of composite linear models via fuzzy clustering. In: Proceedings of European Control Conference 4, Rome, Italy, 1593-1606.
• BABUSKA, R. and VERBRUGGEN, H.B. (1996) An overview of fuzzy modeling for control. Control Engineering Practice 4, 1593-1606.
• BEZDEK, J.C. (1981) Pattern Recognition With Fuzzy Objective Function Algorithms. Plenum Press, New York.
• CHEN, J.Q. and CHEN, J. (1994) An on line identification algorithm for fuzzy systems. Fuzzy Sets and Systems, 63-72.
• FENG, G. (1999) Analysis of new algorithm for continuous time robust adaptive control. IEEE Trans. Automat. Contr. 44, 1764-1768.
• FENG, G. and CHEN, G. (2005) Adaptive control of discrete-time chaotic systems: a fuzzy control approach. Chaos. Solutions and Fractals 23, 459-467.
• GATH, I. and GEVA, A.B. (1989) Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 773-781.
• GLORENNEC, P.Y. (1999) Algorithmes d’apprentissage pour systèmes d’infèrence floue. Hermes Sciences Publications, Paris.
• GUSTAFSON, D.E. and KESSEL, V.C. (1979) Fuzzy clustering, with fuzzy covariance matrix. In: Proceedings IEEE. CDC, San Diego, 761-766.
• HELLENDON, H. and DRIANKOV, D., EDS. (1997) Fuzzy Model Identification: Selected Approaches. Springer, Berlin.
• JANG, J.S.R. (1993) ANFIS: Adaptative-network based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23 (3), 665-685.
• JINXING, Z. and SHIJIUM, L. (1989) Explicit-self-tuning control for a class of nonlinear systems. Automatica 25(4), 593-596.
• JOHANCEN, T.A. and FOSS, B.A. (1993) Constructing NARMAX models using ARMAX models. International Journal of Control 58 (5), 1125-1153.
• NARENDRA, K.S. and ANNASWAMY, A.M. (1989) Stable Adaptive Systems. Prentice Hall, New Jersey.
• NELLES, O., FINK, A., BABUSKA, R. and SETNES, M. (2000) Comparison of two construction algorithms for Takagi-Sugeno fuzzy models. International Journal of Applied Mathematics and Computer Science 10(4), 835-855.
• NELLES, O., FINK, A. and ISERMANN, R. (2000) Local linear model trees (LOLIMOT) toolbox for nonlinear system identification. 12th IFAC Symposium on System Identification (SYSID), Santa Barbara, USA.
• SUGENO, M. and KANG, G.T. (1987) Structure identification of fuzzy model. Fuzzy Sets and Systems 28, 15-33,
• TAKAGI, T.M. and SUGENO, M. (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Transactions on Systems, Man and Cybernetics 15 (1), 116-132.
• TANKA, K. and WANG, H.O. (2001) Fuzzy Control Systems Design and Analysis. A Linear Matrix Inequality Approach. John Wiley and Sons, New York.
• TRABELSI. A., LAFONT, F., KAMOUN, M. and ENEA, G. (2004) Identification of nonlinear multivariable systems by adaptive fuzzy Takagi-Sugeno model. IJCC 2 (3), 137-153.
• WENG, F. and LANG S. (1990) Globally convergent direct adaptive control algorithm for multivariable systems with general time delay structure. International Journal of Control 51(2), 301-314.