Browsing by Author "Chen, Guan-Ming"

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Adaptive fuzzy control using PID-type learning algorithm
(2007-01-01)
Lee, Bore-Kuen
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Hsu, Chen-Fei
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Chen, Guan-Ming
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Lee, Tsu-Tian
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電控工程研究所
This paper proposes a proportional-integral-derivative (PID)-learning-type adaptive fuzzy controller (AFC) for chaotic Duffing dynamic systems. The proposed PID-learning-type AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is designed to mimic an ideal controller and the robust controller is designed to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters are on-line tuned in the sense of Lyapunov theorem, thus the stability of the system can be guaranteed. Finally, a comparison between a conventional AFC and the proposed PID-learning-type AFC is presented. Simulation results verify that the proposed PID-learning-type AFC can achieve better tracking performance and faster tracking error convergence than the conventional AFC for chaotic Duffing dynamic systems.
Fuzzy Control Using Intuitive Image Analysis
(2008)
Chen, Guan-Ming
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Lin, Ping-Zong
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Wang, Wei-Yen
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Lee, Tsu-Tian
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Wang, Chi-Hsu
In this paper, a novel fuzzy control scheme using intuitive image analysis is developed to imitate the intuitive human control behavior determined through human eyes. A CCD camera is used to gather the images of the controlled plant, and a simple algorithm is proposed to analyze the images. Unlike that in the visual servo control systems, the image information is utilized in a more intuitive way via the proposed image analysis algorithm. The difference between a reference image and the current image is numerically expressed and directly used by a fuzzy control system using a human-like control law. To investigate the effectiveness of the proposed fuzzy control scheme, it is applied to an inverted pendulum system. Simulation results show that the proposed scheme can achieve favorable tracking performance without prior knowledge of the controlled plant.
Robust intelligent tracking control with PID-type learning algorithm
(2007-12-01)
Hsu, Chun-Fei
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Chen, Guan-Ming
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Lee, Tsu-Tian
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電控工程研究所
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Institute of Electrical and Control Engineering
This paper proposes a robust intelligent tracking controller (RITC) for a class of unknown nonlinear systems. The proposed RITC system is comprised of a neural controller and a robust controller. The neural controller is designed to approximate an ideal controller using a proportional-integral-derivative (PID)-type learning algorithm in the sense of Lyapunov function, and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Finally, to investigate the effectiveness of the RITC system, the proposed design methodology is applied to control two chaotic dynamical systems. The simulation results verify that the proposed RITC system using PID-type learning algorithm can achieve faster convergence of the tracking error and controller parameters than that using I-type learning algorithm. (c) 2007 Elsevier B.V. All rights reserved.