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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/98858

    Title: On-Line Genetic Algorithm-Based Fuzzy-Neural Sliding Mode Controller Using Improved Adaptive Bound Reduced-Form Genetic Algorithm
    Authors: Lin, Ping-Zong;Wang, Wei-Yen;Lee, Tsu-Tian;Wang, Chi-Hsu
    Contributors: 淡江大學電機工程學系
    Keywords: fuzzy-neural sliding mode controller;adaptive bound reduced-form genetic algorithm;robot manipulator;on-line genetic algorithm-based controller
    Date: 2009-06-01
    Issue Date: 2014-09-24 09:50:56 (UTC+8)
    Publisher: Abingdon: Taylor & Francis
    Abstract: In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.
    Relation: International Journal of Systems Sicence 40(6), pp.571-585
    DOI: 10.1080/00207720902750011
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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