教師著作

Permanent URI for this collectionhttp://rportal.lib.ntnu.edu.tw/handle/20.500.12235/31268

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Author: search.filters.author.Y.-G. LeuHas files: No

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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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    A composite controller for unknown nonlinear dynamical systems using robust adaptive fuzzy-neural control schemes
    (2000-09-27) W.-Y. Wang; C.-C. Hsu; Y.-G. Leu
    A robust adaptive fuzzy-neural control scheme for nonlinear dynamical systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbance and modeling errors. A composite update law, which has a generalized form combining the projection algorithm modification and the switching-σ adaptive law, is used to tune the adjustable parameters for preventing parameter drift and confining states of the system into the specified regions. Moreover, a fuzzy variable structure control method is incorporated into the control law so that the derived controller is robust with respect to unmodeled dynamics, disturbances and modeling errors. Compared with previous control schemes for nonlinear systems, the magnitude of the control input by using the proposed approach is much smaller, which is a significant advantage in designing controllers for practical applications. To demonstrate the effectiveness and applicability of the proposed method, several examples are illustrated in the paper
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    GA-based fuzzy-neural sliding mode controllers of robot manipulators
    (2004-01-01) P.-Z. Lin; W.-Y. Wang; T.-T. Lee; Y.-G. Leu
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    T-S Fuzzy-Neural Control for Robot Manipulators
    (2008-08-25) W.-Y. Wang; Y.-H. Chien; Y.-G. Leu; Z.-H. Lee; T.-T. Lee
    This paper proposes a novel method of on-line modeling and control through the Takagi-Sugeno (T-S) fuzzy-neural model for a class of general n-link robot manipulators. Compared with the previous method, the main contribution of this paper is an investigation of the more general robot systems using on-line adaptive T-S fuzzy-neural controller. Specifically, the general robot systems are exactly formed a linearized system via the mean value theorem, and then the T-S fuzzy-neural model can approximate the linearized system. Also, we propose an on-line identification algorithm and put significant emphasis on robust tracking controller design using an adaptive scheme for the robot systems. Finally, an example including two cases is provided to demonstrate feasibility and robustness of the proposed method.
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    B-spline-based Adaptive Control for a Class of Nonlinear Systems
    (2008-06-07) Z.-H. Lee; W.-Y. Wang; Y.-G. Leu; J.-H. Yang
    本文提供一個以B-spline 為基礎之直接適應性控制方法來控制一個不穩定的未知非線性系統，其設計控制器的主要理論為直接式的適應性控制方法，利用B-spline 數學函數當作設計控制器的基底函數，來建構適應性控制輸入。最後透過matlab 軟體模擬結果證明控制器的設計為有效的。
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    Adaptive Backstepping Control of Nonlinear Systems Using B-spline Neural Networks
    (2008-01-01) C.-Y. Chen; J.-Y. Lin; Z.-H. Lee; Y.-G. Leu; W.-Y. Wang
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    B-spline Adaptive Backstepping Controllers for Affine Nonlinear Systems
    (2009-06-01) Z.-H. Li; W.-Y. Wang; Y.-G. Leu
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    Compact Ant Colony Optimization Algorithm Based Fuzzy Neural Network Backstepping Controller for MIMO Nonlinear Systems
    (2010-07-03) W.-Y. Wang; C.-K. Chen; Y.-G. Leu; C.-Y. Chen
    In this paper, a compact ant colony algorithm used to tune parameters of fuzzy-neural networks is proposed for function approximation and adaptive control of nonlinear systems. In adaptive control procedure for nonlinear systems, weights of the fuzzy neural controller are online adjusted by the compact ant algorithm in order to generate appropriate control input. For the purpose of evaluating the stability of the closed-loop systems, an energy fitness function is used in the ant algorithm. Finally, a computer simulation example demonstrates the feasibility and effectiveness of the proposed method.
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