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Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

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  • Tat-Bao-Thien Nguyen
  • Teh-Lu Liao
  • Jun-Juh Yan

Abstract

In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method.

Suggested Citation

  • Tat-Bao-Thien Nguyen & Teh-Lu Liao & Jun-Juh Yan, 2014. "Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-11, February.
    • Handle: RePEc:hin:jnlmpe:868415
    DOI: 10.1155/2014/868415
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    Cited by:

    1. Yi-Jen Mon, 2022. "Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology," Sustainability, MDPI, vol. 14(9), pages 1-14, April.
    2. Messadi, M. & Mellit, A., 2017. "Control of chaos in an induction motor system with LMI predictive control and experimental circuit validation," Chaos, Solitons & Fractals, Elsevier, vol. 97(C), pages 51-58.

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