Browsing by Author "Hsu, C. -F."

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Robust fuzzy-neural sliding-mode controller design via network structure adaptation
(2008-12-01)
Lin, P. -Z.
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Hsu, C. -F.
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Lee, T. -T.
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Wang, C. -H.
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電控工程研究所
A robust fuzzy-neural sliding-mode control (RFSC) scheme for unknown nonlinear systems is proposed. The RFSC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy-neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L(2) tracking performance. The SFNN identifier uses the structure- and parameter-learning phases to perform the estimation of the unknown system dynamics. The structure-learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. Finally, the proposed RFSC system is applied to three nonlinear dynamic systems. The simulation results show that the proposed RFSC system can achieve favourable tracking performance by incorporating SFNN identifier, sliding-mode control and robust control techniques.
Supervisory intelligent control system design for forward DC-DC converters
(2006-09-01)
Hsu, C. -F.
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Lin, C. -M.
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Cheng, K. -H.
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電控工程研究所
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Institute of Electrical and Control Engineering
A supervisory intelligent control system is developed. The supervisory intelligent control system is comprised of a neural controller and a supervisory controller. The neural controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the neural controller and the ideal controller. In the proposed control scheme, an online parameter training methodology is developed based on the gradient descent method and the Lyapunov stability theorem, so that the control system can guarantee system stability. Finally, to investigate the effectiveness of the proposed control scheme, it is applied to control a forward DC-DC converter. A comparison between a PI controller, a fuzzy controller, a fuzzy neural network controller and the supervisory intelligent controller is made. Experimental results show that the proposed control system can achieve favourable regulation performances even for different input voltages and under load resistance variations.