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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/99870

    Title: Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
    Authors: Hsu, Chun-Fei
    Contributors: 淡江大學電機工程學系
    Date: 2013-05
    Issue Date: 2015-01-05 10:28:31 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: This paper presents an adaptive PI Hermite neural control (APIHNC) system for multi-input multi-output (MIMO) uncertain nonlinear systems. The proposed APIHNC system is composed of a neural controller and a robust compensator. The neural controller uses a three-layer Hermite neural network (HNN) to online mimic an ideal controller and the robust compensator is designed to eliminate the effect of the approximation error introduced by the neural controller upon the system stability in the Lyapunov sense. Moreover, a proportional–integral learning algorithm is derived to speed up the convergence of the tracking error. Finally, the proposed APIHNC system is applied to an inverted double pendulums and a two-link robotic manipulator. Simulation results verify that the proposed APIHNC system can achieve high-precision tracking performance. It should be emphasized that the proposed APIHNC system is clearly and easily used for real-time applications.
    Relation: Applied Soft Computing 13(5), pp.2569–2576
    DOI: 10.1016/j.asoc.2012.11.029
    Appears in Collections:[Graduate Institute & Department of Electrical Engineering] Journal Article

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