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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98859


    Title: Robust wavelet-based adaptive neural controller design with a fuzzy compensator
    Authors: Hsu, Chun-Fei;Cheng, Kuo-Hsiang;Lee, Tsu-Tian
    Contributors: 淡江大學電機工程學系
    Keywords: Adaptive control;Neural control;Chaotic system;Fuzzy compensation;Wavelet neural network
    Date: 2009-12-01
    Issue Date: 2014-09-24 09:52:08 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: In this paper, a robust wavelet-based adaptive neural control (RWANC) with a PI type learning algorithm is proposed. The proposed RWANC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena of conventional sliding-mode control completely. In the RWANC, the learning algorithm is derived based on the Lyapunov function, thus the closed-loop system's stability can be guaranteed. The chaotic system control has become an emerging topic in engineering community since the uncontrolled system displays complex, noisy-like and unpredictable behavior. Therefore, the proposed RWANC approach is applied to a second-order chaotic nonlinear system to investigate the effectiveness. Through the simulation results, the proposed RWANC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm.
    Relation: Neurocomputing 73(1–3), pp.423–431
    DOI: 10.1016/j.neucom.2009.07.011
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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