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Discrete Dynamics in Nature and Society
Volume 2014 (2014), Article ID 315634, 12 pages
Research Article

Output Feedback Adaptive Dynamic Surface Control of Permanent Magnet Synchronous Motor with Uncertain Time Delays via RBFNN

1State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China
2Department of Mechanical Engineering, Chongqing Aerospace Polytechnic College, Chongqing 400021, China

Received 25 July 2013; Revised 26 November 2013; Accepted 3 December 2013; Published 2 January 2014

Academic Editor: M. De la Sen

Copyright © 2014 Shaohua Luo et al. 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 focuses on an adaptive dynamic surface control based on the Radial Basis Function Neural Network for a fourth-order permanent magnet synchronous motor system wherein the unknown parameters, disturbances, chaos, and uncertain time delays are presented. Neural Network systems are used to approximate the nonlinearities and an adaptive law is employed to estimate accurate parameters. Then, a simple and effective controller has been obtained 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 control has been illustrated through simulation results.