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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/104565


    Title: Intelligent complementary sliding-mode control with dead-zone parameter modification
    Authors: Hsu, Chun-Fei;Kuo, Tzu-Chun
    Keywords: Neural control;Sliding-mode control;Neural fuzzy inference network;Recurrent neural network
    Date: 2014-10-01
    Issue Date: 2016-01-06 11:03:02 (UTC+8)
    Abstract: This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L2-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L2-gain robust control approach by the proposed ICSMC scheme.
    Relation: Applied Soft Computing 23, pp.355-365
    DOI: 10.1016/j.asoc.2014.06.008
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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