Browsing by Author "Hsu, Chun-Fei"

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Adaptive asymmetric fuzzy neural network controller design via network structuring adaptation
(2008-10-16)
Hsu, Chun-Fei
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Lin, Ping-Zong
;
Lee, Tsu-Tian
;
Wang, Chi-Hsu
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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.
Adaptive backstepping tracking control using self-organizing fuzzy neural network
(2006-01-01)
Lin, Chih-Min
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Hsu, Chun-Fei
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Chung, I-Fang
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This papers proposes an adaptive backstepping tracking control (ABTC) via self-organizing fuzzy neural network (SOFNN) approach. The proposed ABTC system is comprised of a backstepping tracking controller and an L, controller. The backstepping tracking controller containing a SOFNN identifier is the principal controller, and the L, controller is designed to achieve a tracking performance with desired attenuation level. The SOFNN identifier is used to online estimate the system dynamics with structure and parameter learning. Finally, the proposed ABTC is applied to control a chaotic dynamic system. The simulation results verify that the proposed ABTC system can achieve favorable tracking performance by incorporating of neural network approach and adaptive backstepping control technique.
Adaptive neuro-wavelet control for switching power supplies
(2007-01-01)
Lin, Chih-Min
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Hung, Kun-Neng
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Hsu, Chun-Fei
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電控工程研究所
;
Institute of Electrical and Control Engineering
The switching power supplies can convert one level of electrical voltage into another level by switching action. They are very popular because of their high efficiency and small size. This paper proposes an adaptive neuro-wavelet (ANW) control system for the switching power supplies. In the ANW control system, a neural controller is the main controller used to mimic an ideal controller and a compensated controller is designed to recover the residual of the approximation error. In this study, an online adaptive law with a variable optimal learning-rate is derived based on the Lyapunov stability theorem, so that not only the stability of the system can be guaranteed but also the convergence of controller parameters can be speeded up. Then, the proposed ANW control system is applied to control a forward switching power supply. Experimental results show that the proposed ANW controller can achieve favorable regulation performance for the switching power supply even under input voltage and load resistance variations.
DC-DC intelligent power regulator design using wavelet neural network
(2007)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Hung, Kun-Neng
;
電機學院
;
College of Electrical and Computer Engineering
DC-DC power converters are the! devices which can convert a certain electrical voltage to another level of electrical voltage by switching action. This paper proposes a wavelet neural network control (WNNC) system for the DC-DC power converters. The WNNC system is comprised of a neural controller and a robust controller. The neural controller is used to mimic an ideal controller and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Finally, some experimental results are provided to demonstrate the proposed WNNC system can track the output reference voltage variations and cope with the load resistance variations to ensure the stability while providing fast transient response.
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
;
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.
Intelligent backstepping control for chaotic systems using self-growing fuzzy neural network
(2006)
Lin, Chih-Min
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Hsu, Chun-Fei
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Chung, I-Fang
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電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes an intelligent backstepping control (IBC) for the chaotic systems. The IBC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-growing fuzzy neural network (SGFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SGFNN identifier. Finally, simulation results verify that the IBC system can achieve favorable tracking performance.
Intelligent control for DC-DC power converter with recurrent fuzzy neural network approach
(2006)
Hsu, Chun-Fei
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Lee, Tsu-Tian
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Wen, Yao-Wei
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Ding, Fu-Shan
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電控工程研究所
For many years, the control approaches for the dc-dc power converters are limited to PI controller structures. However, it gives the overshoot in output voltage as the rise time of response is reduced. To tackle this problem, an adaptive recurrent fuzzy neural network (ARFNN) control system is developed in this paper. The on-line adaptive laws of the ARFNN control scheme are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. Experimental results show that the proposed ARFNN control scheme can achieve good regulation performances.
Intelligent tracking control for Duffing oscillator using a self-organizing fuzzy neural network
(2006)
Chen, Te-Yu
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Hsu, Chun-Fei
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Lee, Tsu-Tian
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Ding, Fu-Shan
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Chiu, Chih-Hui
This paper proposes an intelligent tracking control for the chaotic systems via backstepping approach. The intelligent tracking control system is comprised of a neural controller and a robust controller. The neural controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. Finally, simulation results verify that the proposed intelligent tracking control can achieve favorable tracking performance.
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
;
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.
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
;
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.
Wavelet adaptive backstepping control for a class of nonlinear systems
(2006-09-01)
Hsu, Chun-Fei
;
Lin, Chih-Min
;
Lee, Tsu-Tian
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper proposes a wavelet adaptive backstepping control (WABC) system for a class of second-order nonlinear systems. The WABC comprises a neural backstepping controller and a robust controller. The neural backstepping controller containing a wavelet neural network (WNN) identifier is the principal controller, and the robust controller is designed to achieve L-2 tracking performance with desired attenuation level. Since the WNN uses wavelet functions, its learning capability is superior to the conventional neural network for system identification. Moreover, the adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalat's lemma, thus the system can be guaranteed to be asymptotically stable. The proposed WABC is applied to two nonlinear systems, a chaotic system and a wing-rock motion system to illustrate its effectiveness. Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L-2 robust control techniques.