教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Author: search.filters.author.M.-L. ChanSubject: search.filters.subject.tracking performance

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    H-inf. tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach
    (IEEE Systems, Man, and Cybernetics Society, 2002-08-01) W.-Y. Wang; M.-L. Chan; C.-C. James Hsu; T.-T. Lee
    A novel adaptive fuzzy-neural sliding-mode controller with H∞ 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∞ 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∞ 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
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    Adaptive fuzzy control for strict-feedback canonical nonlinear systems with H-inf. tracking performance
    (IEEE Systems, Man, and Cybernetics Society, 2000-12-01) W.-Y. Wang; M.-L. Chan; T.-T. Lee; C.-H. Liu
    In this paper, an adaptive fuzzy controller for strict-feedback canonical nonlinear systems is proposed. The completely unknown nonlinearities and disturbances of the systems are considered. Since fuzzy logic systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory is employed to derive the control law for the strict-feedback system with unknown nonlinear functions and disturbances. Moreover, H∞ tracking performance is applied to substantially attenuate the effect of the modeling errors and disturbances. Finally, examples are simulated to confirm the applicability of the proposed methods.