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URN etd-0712104-165855 Statistics This thesis had been viewed 3299 times. Download 1014 times. Author Hsing-Hung Liu Author's Email Address firstname.lastname@example.org Department Electrical Engineering Year 2003 Semester 2 Degree Master Type of Document Master's Thesis Language English Page Count 44 Title CMAC INTEGRAL VARIABLE STRUCTURE CONTROLLER DESIGN FOR UNKNOWN NONLINEAR SYSTEM Keyword sliding mode control nonlinear system neural nertwork integral variable structure control CMAC CMAC integral variable structure control neural nertwork nonlinear system sliding mode control Abstract In this thesis, we present a novel integral variable structure control (IVSC) that combines a cerebellar model articulation controller (CMAC) neural network and a soft supervisor controller for use in designing unknown nonlinear system. Since the nonlinear function of system are not known exactly, so using the CMAC neural network learning approach to estimate them, and apply update law to adjust the weighting factors online, and perform the equivalent control on IVSC. Based on the Lyapunov theorem, the soft supervisor controller is designed to guarantee the global stability of the system. The proposed IVSC control scheme alleviates the dependency on system parameters and eliminates the chattering of the control signal through an efficient learning scheme. The CMAC-based IVSC (CIVSC) scheme is proven to be globally stable inasmuch all signals involved are bounded and the tracking error converges to zero.
We apply the CIVSC scheme to compare with the adaptive fuzzy sliding mode controller (AFSMC) in an inverted pendulum balancing problem. The simulation results demonstrate the effectiveness and robustness of the proposed controller.
Advisor Committee Hung-Ching Lu - advisor
Ming-Feng Yeh - co-chair
Ta-Hsiung Hung - co-chair
Files Date of Defense 2004-06-28 Date of Submission 2004-07-12