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Abstract and Applied Analysis
Volume 2014, Article ID 609340, 9 pages
Research Article

Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network

1School of Automation, Chongqing University, Chongqing 400044, China
2Department of Mechanical Engineering, Chongqing Aerospace Polytechnic College, Chongqing 400021, China

Received 4 April 2014; Accepted 7 May 2014; Published 22 June 2014

Academic Editor: Jun Hu

Copyright © 2014 Shaohua Luo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper is concerned with the problem of the nonlinear dynamic surface control (DSC) of chaos based on the minimum weights of RBF neural network for the permanent magnet synchronous motor system (PMSM) wherein the unknown parameters, disturbances, and chaos are presented. RBF neural network is used to approximate the nonlinearities and an adaptive law is employed to estimate unknown parameters. Then, a simple and effective controller is designed by introducing dynamic surface control technique on the basis of first-order filters. Asymptotically tracking stability in the sense of uniformly ultimate boundedness is achieved in a short time. Finally, the performance of the proposed controller is testified through simulation results.