H∞ neural networks control for uncertain nonlinear switched impulsive systems

Authors:
Fei Long;Shumin Fei;Zhumu Fu;Shiyou Zheng
Affiliations:
School of Informational Engineering, Guizhou University, Guiyang, China and Department of Automatic Control, Southeast University, Nanjing, China;Department of Automatic Control, Southeast University, Nanjing, China;Department of Automatic Control, Southeast University, Nanjing, China;Department of Automatic Control, Southeast University, Nanjing, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

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Abstract

Based on RBF (radial basis function) neural network, an adaptive neural network feedback control scheme and an impulsive controller for output tracking error disturbance attenuation of nonlinear switched impulsive systems are given under all admissible switched strategy in this paper. Impulsive controller is designed to attenuate effect of switching impulse. The RBF neural network is used to compensate adaptively for the unknown nonlinear part of switched impulsive systems, and the approximation error of RBF neural network is introduced to the adaptive law in order to improve the tracking attenuation quality of the switched impulsive systems. Under all admissible switching law, impulsive controller and adaptive neural network feedback controller can guarantee asymptotic stability of tracking error and improve disturbance attenuation level of tracking error for the overall switched impulsive system.