Browsing by Author "Chan, ML"

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Adaptive fuzzy sliding mode controller for linear systems with mismatched time-varying uncertainties
(2003-04-01)
Tao, CW
;
Chan, ML
;
Lee, TT
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A new design approach of an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented in this paper. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching condition satisfied, an AFSMC. is proposed to stabilize the uncertain system. The parameters of the output fuzzy sets in the fuzzy mechanism are on-line adapted to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented AFSMC. The stability of the fuzzy control system is guaranteed and the system is shown to be invariant on the sliding surface. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design. approach. Simulation results are included to,illustrate the effectiveness of the proposed AFSMC.
H-infinity tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach
(2002-08-01)
Wang, WY
;
Chan, ML
;
Hsu, CCJ
;
Lee, TT
;
電控工程研究所
In this paper, a novel adaptive fuzzy-neural sliding mode controller with H-infinity tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H-infinity tracking design technique and the sliding mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H-infinity tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.