教師著作

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

Browse

Author: search.filters.author.Y.-G. LeuSubject: search.filters.subject.nonlinear systems

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Adaptive Nonlinear Parametric Neural Control of Nonaffine Nonlinear Systems
    (2010-07-03) Y.-G. Leu; W.-C. Leu; W.-Y. Wang; Z.-H. Lee
    By using B-spline neural networks, an adaptive nonlinear parametric control scheme for nonlinear systems is proposed in this paper. The control scheme which is utilized to design the control input incorporates the adaptive control design technique with the mean-estimation B-spline neural networks. Compared with other neural networks, the B-spline neural networks possess output behavior the characteristic feature of locally controlling. Therefore, they are very suitable to online estimate system dynamics by tuning both control and knot points. The B-spline neural networks with a mean苟stimation technique are used in an attempt to avoid difficulty of differentiating B-spline basis functions. In addition, two robust controllers are used to compensate un modeling dynamics. Finally, an example is provided to demonstrate the feasibility of the proposed scheme, and a comparative study is given by computer simulation.
  • No Thumbnail Available
    Item
    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
  • No Thumbnail Available
    Item
    Robust adaptive fuzzy-neural control of nonlinear dynamical systems using generalized projection update law and variable structure controller
    (IEEE Systems, Man, and Cybernetics Society, 2001-02-01) W.-Y. Wang; Y.-G. Leu; C.-C. Hsu
    In this paper, 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 generalized projection update law, which generalizes 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 to the specified regions. Moreover, a 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. To demonstrate the effectiveness of the proposed method, several examples are illustrated in this paper