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

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  • Shaohua Luo

Abstract

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.

Suggested Citation

  • Shaohua Luo, 2014. "Nonlinear Dynamic Surface Control of Chaos in Permanent Magnet Synchronous Motor Based on the Minimum Weights of RBF Neural Network," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:609340
    DOI: 10.1155/2014/609340
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    References listed on IDEAS

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    1. Zribi, Mohamed & Oteafy, Ahmed & Smaoui, Nejib, 2009. "Controlling chaos in the permanent magnet synchronous motor," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1266-1276.
    2. de Paula, Aline Souza & Savi, Marcelo Amorim, 2009. "A multiparameter chaos control method based on OGY approach," Chaos, Solitons & Fractals, Elsevier, vol. 40(3), pages 1376-1390.
    3. Alasty, Aria & Salarieh, Hassan, 2005. "Controlling the chaos using fuzzy estimation of OGY and Pyragas controllers," Chaos, Solitons & Fractals, Elsevier, vol. 26(2), pages 379-392.
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