Browsing by Author "Chen, Pin-Cheng"

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A Dynamic Neural Network Model for Nonlinear System Identification
(2008)
Wang, Chi-Hsu
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Chen, Pin-Cheng
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Lin, Ping-Zong
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Lee, Tsu-Tian
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電控工程研究所
In this paper, a new dynamic neural network based on the Hopfield neural network is proposed to perform the nonlinear system identification. Convergent analysis is performed by the Lyapunov-like criterion to guarantee the error convergence during identification. Simulation results demonstrate that the proposed dynamic neural network trained by the Lyapunov approach can obtain good identifted performance.
Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system
(2009-05-01)
Chen, Pin-Cheng
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Hsu, Chun-Fei
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Lee, Tsu-Tian
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Wang, Chi-Hsu
;
電控工程研究所
In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.
Robust Adaptive Control Scheme Using Hopfield Dynamic Neural Network for Nonlinear Nonaffme Systems
(2010)
Chen, Pin-Cheng
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Lin, Ping-Zing
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Wang, Chi-Hsu
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Lee, Tsu-Tian
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電機工程學系
In this paper, we propose a robust adaptive control scheme using Hopfield-based dynamic neural network for uncertain or ill-defined nonlinear nonaffine systems. A Hopfield-based dynamic neural network is used to approximate the unknown plant nonlinearity. The robust adaptive controller is designed to achieve a L-2 tracking performance to stabilize the closed-loop system. The weights of Hop field-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed, and the tracking error is bounded. The proposed control scheme is applied to control an anti-lock braking system, and the simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.