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Synchronous reluctance motor speed drive using sliding mode controller based on Gaussian radial basis function neural network

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Abstract

In this article, a sliding mode control (SMC) design based on a Gaussian radial basis function neural network (GRBFNN) is proposed for a synchronous reluctance motor (SynRM) system robust stabilization and disturbance rejection. This method utilizes the Lyapunov function and the steep descent rule to guarantee the convergence of the SynRM drive system asymptotically. Finally, we employ experiments to validate the proposed method.

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Author information

Authors and Affiliations

  1. Electronic Vehicle and System Verification Group R & D Division, Automotive Research and Testing Center, Changhua, 50544, Taiwan, ROC

    Chien-An Chen

  2. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, 640, Taiwan, ROC

    Huann-Keng Chiang

  3. School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, 640, Taiwan, ROC

    Wen-Bin Lin

  4. Department of Electrical Engineering, Nan-Jeon Institute of Technology, Yen-Shui, Tainan, 737, Taiwan, ROC

    Chih-Huang Tseng

Authors
  1. Chien-An Chen
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  2. Huann-Keng Chiang
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  3. Wen-Bin Lin
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  4. Chih-Huang Tseng
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Corresponding author

Correspondence to Huann-Keng Chiang.

Additional information

This work was presented in part at the 14th International Symposium on Artificial Life and Robotics, Oita, Japan, February 5–7, 2009

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Chen, CA., Chiang, HK., Lin, WB. et al. Synchronous reluctance motor speed drive using sliding mode controller based on Gaussian radial basis function neural network. Artif Life Robotics 14, 53–57 (2009). https://doi.org/10.1007/s10015-009-0627-8

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  • DOI: https://doi.org/10.1007/s10015-009-0627-8

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