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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/53599

    Title: On GA-based Optimal Fuzzy Control
    Authors: Lin, Sinn-cheng;Chen, Yung-yaw
    Contributors: 淡江大學資訊與圖書館學系
    Keywords: Fuzzy control;Optimal control;Fuzzy sliding-mode control;Genetic algorithms
    Date: 1995-11
    Issue Date: 2011-05-20 09:51:07 (UTC+8)
    Abstract: Two architectures for designing optimal fuzzy control systems were proposed in this paper. In both cases, the membership functions in the fuzzy rulebases were tuned by the genetic algorithms. The objective was to explore a fuzzy controller by minimizing a quadratic cost fkction. In the first architecture, the employed controller was a conventional, fuzzy logic controller which used the system states as input variables. Consequently, the reciprocal of the cost function to be minimized could be directly applied towards evaluating the fitness of the controller. In the second architecture, a $my sliding mode controller was adopted. The combined information of the system states, i.e. the sliding function, formed a single input variable. The problem of minimizing the cost function in this case could be transformed to that of deriving an optimal sliding surface. Then, a faster hitting time and a smaller distance away from the sliding surface a controller had, a lugher fitness it got. Simulations and comparisons were taken on both cases.
    Relation: IEEE International Conference on Evolutionary Computing,  Perth, Australia
    Appears in Collections:[Graduate Institute & Department of Information and Library Sciences] Proceeding

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