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SVM Based Nonlinear Self-tuning Control

  • Weimin Zhong
  • Daoying Pi
  • Chi Xu
  • Sizhen Chu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper, a support vector machine (SVM) with polynomial kernel function enhanced nonlinear self-tuning controller is developed, which combines the SVM identifier and parameters’ modifier together. The inverse model of a nonlinear system is achieved by off-line black-box identification according to input and output data. Then parameters of the model are modified online using gradient descent algorithm. Simulation results show that SVM based self-tuning control can be well applied to nonlinear uncertain system. And the SVM based self-tuning control of nonlinear system has good robustness performance in tracking reference input with good generalization ability.

Keywords

Support Vector Machine Inverse Model Reference Input Gradient Descent Algorithm Nonlinear Uncertain System 
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

  • Weimin Zhong
    • 1
    • 2
  • Daoying Pi
    • 1
  • Chi Xu
    • 3
  • Sizhen Chu
    • 3
  1. 1.National Laboratory of Industrial Control Technology, Institute of Modern Control EngineeringZhejiang UniversityHangzhouP.R. China
  2. 2.Automation Institute of East ChinaUniversity of Science and TechnologyShanghaiP.R. China
  3. 3.Hangzhou Automation Technology InstituteHangzhouP.R. China

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