H ∞  Neural Networks Control for Uncertain Nonlinear Switched Impulsive Systems

  • Fei Long
  • Shumin Fei
  • Zhumu Fu
  • Shiyou Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


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 net-work is used to compensate adaptively for the unknown nonlinear part of switched impulsive systems, and the approximation error of RBF neural net-work 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.


Tracking Error Impulsive System Disturbance Attenuation Neural Network Control Adaptive Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fei Long
    • 1
    • 2
  • Shumin Fei
    • 2
  • Zhumu Fu
    • 2
  • Shiyou Zheng
    • 2
  1. 1.School of Informational EngineeringGuizhou UniversityGuiyangChina
  2. 2.Department of Automatic ControlSoutheast UniversityNanjingChina

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