淡江大學機構典藏:Item 987654321/115961
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/115961


    Title: An on-line robust and adaptive T-S fuzzy-neural controller for more general unknown systems
    Authors: Wang, W.Y.;Chien, Y.H.;Li, I.H.
    Date: 2008-03
    Issue Date: 2019-03-14 12:10:22 (UTC+8)
    Publisher: Springer
    Abstract: This paper proposes a novel method of on-line modeling via the Takagi-Sugeno (T-S) fuzzy-neural model and robust adaptive control for a class of general unknown nonaffine nonlinear systems with external disturbances. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known on the more complicated and general nonlinear systems. Compared with the previous approaches, the contribution of this paper is an investigation of the more general unknown nonaffine nonlinear systems using on-line adaptive T-S fuzzy-neural controllers. Instead of modeling these unknown systems directly, the T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS), with modeling errors and external disturbances. We prove that the closed-loop system controlled by the proposed controller is robust stable and the effect of all the unmodeled dynamics, modeling errors and external disturbances on the tracking error is attenuated under mild assumptions. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.
    Relation: International Journal of Fuzzy Systems 10(1), p.33-43
    Appears in Collections:[Graduate Institute & Department of Mechanical and Electro-Mechanical Engineering] Journal Article

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