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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/60930

    Title: Fuzzy-neural-based control for nonlinear time-varying delay systems
    Authors: 黃志良
    Contributors: 淡江大學電機工程學系
    Keywords: Fuzzy control;Fuzzy systems;Nonlinear control systems;Control systems;Time varying systems;Delay systems;Uncertainty;Fuzzy sets;Adaptive control;Robust control
    Date: 2007-12
    Issue Date: 2011-10-15 01:06:36 (UTC+8)
    Abstract: In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.
    Relation: IEEE Transaction on Systems, Man & Cybernetics, Part B- Cybernetics 37(6), pp.1471-1485
    DOI: 10.1109/TSMCB.2007.903448
    Appears in Collections:[電機工程學系暨研究所] 期刊論文

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