教師著作
Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268
Browse
2 results
Search Results
Item A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes(2000-09-27) W.-Y. Wang; C.-C. Hsu; Y.-G. LeuA robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paperItem 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. LeeA 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