Fuzzy Control Scheme for a Class of MIMO Systems with Uncertainties

Based on the Lyapunov synthesis approach and regarding the fuzzy systems as approximators to approximate the unknown functions in the system to be controlled, several adaptive fuzzy control schemes have been developed during the last decade. Actually, these schemes have been applied only to simple classes of nonlinear systems. In the concrete, (i) most of them just consider SISO systems (which can avoid the challenging of the coupling between control inputs); (ii) the upper bounds of uncertainties, and the reconstruction errors between the best approximators and their corresponding functions to be approximated are assumed to be known (in this way, the traditional adaptive methods or robust methods could be utilized straightforwardly). This paper develops a design methodology that expands the class of nonlinear systems to MIMO systems, the above restrictive assumptions can be relaxed by using an unique way to deal with the uncertainties and the reconstruction errors. The overall adaptive scheme is shown to guarantee the tracking error, between the outputs of system and the desired values, to be asymptotical in decay.

[1]  Hugang Han,et al.  Robust fuzzy control of nonlinear systems using shape-adaptive radial basis functions , 2002, Fuzzy Sets Syst..

[2]  Shaocheng Tong,et al.  Fuzzy direct adaptive control for a class of nonlinear systems , 1999, Fuzzy Sets Syst..

[3]  S. Sastry,et al.  Adaptive Control: Stability, Convergence and Robustness , 1989 .

[4]  Chun-Yi Su,et al.  Adaptive control of a class of nonlinear systems with fuzzy logic , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[5]  Zeungnam Bien,et al.  Design of fuzzy direct adaptive controller and stability analysis for a class of nonlinear system , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[6]  L X Wang,et al.  Fuzzy basis functions, universal approximation, and orthogonal least-squares learning , 1992, IEEE Trans. Neural Networks.

[7]  Shaocheng Tong,et al.  A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems , 2003, IEEE Trans. Fuzzy Syst..

[8]  Kevin M. Passino,et al.  Decentralized adaptive control of nonlinear systems using radial basis neural networks , 1999, IEEE Trans. Autom. Control..

[9]  Hugang Han,et al.  Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators , 2001, IEEE Trans. Fuzzy Syst..

[10]  Jean-Jacques E. Slotine,et al.  Sliding controller design for non-linear systems , 1984 .

[11]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[12]  Marios M. Polycarpou,et al.  Stable adaptive neural control scheme for nonlinear systems , 1996, IEEE Trans. Autom. Control..

[13]  Li-Xin Wang Stable adaptive fuzzy control of nonlinear systems , 1993, IEEE Trans. Fuzzy Syst..

[14]  Fuchun Sun,et al.  Stable neural-network-based adaptive control for sampled-data nonlinear systems , 1998, IEEE Trans. Neural Networks.

[15]  Shaocheng Tong,et al.  Fuzzy adaptive sliding-mode control for MIMO nonlinear systems , 2003, IEEE Trans. Fuzzy Syst..

[16]  Rainer Palm,et al.  Robust control by fuzzy sliding mode , 1994, Autom..

[17]  Tsung-Chih Lin,et al.  Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems , 2002, IEEE Trans. Fuzzy Syst..

[18]  Robert M. Sanner,et al.  Gaussian Networks for Direct Adaptive Control , 1991, 1991 American Control Conference.