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Multiple Models Adaptive Control Based on RBF Neural Network Dynamic Compensation

  • Junyong Zhai
  • Shumin Fei
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

A novel multiple models adaptive control method is proposed to improve the dynamic performance of complex nonlinear systems under different operating modes. Multiple linearized models are established at each equilibrium point of the system. Each local linearized model is valid within a neighborhood of the point, and then an improved RBF algorithm is applied to compensate for modeling error. Simulation results are presented to demonstrate the validity of the proposed method.

Keywords

Equilibrium Point Adaptive Control Hide Node Radial Basis Function Neural Network Multiple Input Single Output 
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 2005

Authors and Affiliations

  • Junyong Zhai
    • 1
  • Shumin Fei
    • 1
  1. 1.Research Institute of AutomationSoutheast UniversityNanjingChina

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