Browsing by Author "Lin, Ping-Zong"

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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.
Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation
(2008-10-16)
Hsu, Chun-Fei
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Lin, Ping-Zong
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Lee, Tsu-Tian
;
Wang, Chi-Hsu
;
電控工程研究所
This paper proposes a self-structuring fuzzy neural network (SFNN) using asymmetric Gaussian membership functions in the structure and parameter learning phases. An adaptive self-structuring asymmetric fuzzy neural-network control (ASAFNC) system which consists of an SFNN controller and a robust controller is proposed. The SFNN controller uses an SFNN with structure and parameter learning phases to online mimic an ideal controller, simultaneously. The structure learning phase consists of the growing-and-pruning algorithms of fuzzy rules to achieve an optimal network structure, and the parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. The robust controller is designed to compensate for the modeling error between the SFNN controller and the ideal controller. An online training methodology is developed in the Lyapunov sense, and thus the stability of the closed-loop control system can be guaranteed. Finally, the proposed ASAFNC system is applied to a second-order chaotic dynamics system. The simulation results show that the proposed ASAFNC can achieve favorable tracking performance. (C) 2008 Elsevier B.V. All rights reserved.
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.
Intelligent Tracking Controller Design Using Dynamic Fuzzy Neural Networks
(2007)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Lin, Ping-Zong
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電控工程研究所
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Institute of Electrical and Control Engineering
An intelligent tracking control using a dynamic fuzzy neural network (DFNN) is proposed in this paper. The intelligent tracking control system is comprised of a computation controller and a robust controller. The computation controller containing a DFNN identifier is the principal controller, and the robust controller is designed to achieve L, tracking performance. The DFNN identifier uses the structure and parameter learning phases to online estimate the unknown control dynamics equation. Finally, the proposed intelligent tracking control system is applied to control a second-order chaotic circuit system. The simulation results show that the proposed intelligent tracking control system can achieve favorable tracking performance by incorporating of DFNN identification, sliding-mode control and robust control techniques.
On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm
(2009)
Lin, Ping-Zong
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Wang, Wei-Yen
;
Lee, Tsu-Tian
;
Wang, Chi-Hsu
;
電控工程研究所
In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.
Robust self-organizing fuzzy-neural control using asymmetric Gaussian membership functions
(2007-06-01)
Lin, Ping-Zong
;
Lee, Tsu-Tian
;
電控工程研究所
;
Institute of Electrical and Control Engineering
A robust self-organizing fuzzy-neural control (RSOFNC) system is proposed in this paper. The RSOFNC system is comprised of a self-structuring fuzzy neural network (SFNN) controller and a robust controller. The SFNN controller is the principal controller and the robust controller is designed to achieve L-2 tracking performance. In the SFNN controller design, a SFNN with the asymmetric Gaussian membership functions is used to online approximate an ideal controller via the structure and parameter learning phases. The structure learning phase consists of the growing of membership functions and the pruning of fuzzy rules, and thus the SFNN can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RSOFNC system is applied to control a second-order chaotic system. The simulation results show that the proposed RSOFNC system can achieve favorable tracking performance.