A Discrete-Time System Adaptive Control Using Multiple Models and RBF Neural Networks

  • Jun-Yong Zhai
  • Shu-Min Fei
  • Kan-Jian Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


A new control scheme using multiple models and RBF neural networks is developed in this paper. The proposed scheme consists of multiple feedback linearization controllers, which are based on the known nominal dynamics model and a compensating controller, which is based on RBF neural networks. The compensating controller is applied to improve the transient performance. The neural network is trained online based on Lyapunov theory and learning convergence is thus guaranteed. Simulation results are presented to demonstrate the validity of the proposed method.


Adaptive Control Multiple Model Lyapunov Theory IEEE Control System Magazine NARMAX Model 
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

  • Jun-Yong Zhai
    • 1
  • Shu-Min Fei
    • 1
  • Kan-Jian Zhang
    • 1
  1. 1.Research Institute of AutomationSoutheast UniversityNanjingChina

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