教師著作

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End date: 1999Subject: search.filters.subject.Fuzzy control

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    以DSP基礎建立即時模糊類神經網路之研究
    (行政院國家科學委員會, 1998-07-31) 王偉彥
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    H-inf.-observer-based adaptive fuzzy-neural control for a class of uncertain nonlinear systems
    (1999-10-15) Y.-G. Leu; W.-Y. Wang; T.-T. Lee
    This paper presents a method for designing an H∞-observer-based adaptive fuzzy-neural output feedback control law with on-line tuning of fuzzy-neural weighting factors for a class of uncertain nonlinear systems based on the H∞ control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach. The H∞-observer-based output feedback control law guarantees that all signals involved are bounded and provides the modeling error (and the external bounded disturbance) attenuation with H∞ performance, obtained by a Riccati-Like equation. Besides, the H∞-observer-based output feedback control law doesn't require the assumptions of the total system states available for measurement and the uncertain system nonlinearities only restricted to the system output. Finally, an example is simulated in order to confirm the effectiveness and applicability of the proposed methods
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    Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems
    (IEEE Systems, Man, and Cybernetics Society, 1999-10-01) Y.-G. Leu; T.-T. Lee; W.-Y. Wang
    In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance