Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/20151
Title: Neural networks based nonlinear H∞ control for linear switched reluctance motor
Authors: Li, HY
Liu, YL
Wang, J
Wong, YK
Chan, WL 
Keywords: Hinfin control
Lyapunov methods
Linear motors
Machine control
Neurocontrollers
Nonlinear control systems
Position control
Reluctance motors
Stability
Uncertain systems
Issue Date: 2009
Publisher: IEEE
Source: ASCC 2009 : the proceedings of 2009 7th Asian Control Conference : Hong Kong Convention and Exhibition Centre, Hong Kong : August 27-29, 2009, p. 796-801 How to cite?
Abstract: This paper is concerned with Hinfin control problems for a class of uncertain nonlinear systems. In the procedure, neural networks (NNs) are used to model the nonlinear functions, Hinfin tracking controller is derived based on Lyapunov function and the notion of dissipativeness. The controller can not only guarantee the stability of the overall control system, but also attenuate the effect of both the external disturbance and NNs approximation error to a prescribed level. Furthermore, theoretical results are applied to a position tracking control of linear switched reluctance motor. Simulation studies are included to demonstrate the effectiveness of the method.
URI: http://hdl.handle.net/10397/20151
ISBN: 978-89-956056-2-2
978-89-956056-9-1 (E-ISBN)
Appears in Collections:Conference Paper

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