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The neural sliding mode controller design of fan-plate system

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Abstract

This paper proposed a sliding mode angle control with neural network estimator design for a fan-plate system. The neural network estimator is based on radial basis function and it estimates the unknown lumped bounded uncertainty of parameter variations and external disturbances in real-time. The abilities of anti-disturbance and anti-chattering are better than conventional sliding mode controller and adaptive sliding mode controller. The Lyapunov stability theorem is employed to ensure the stability of the proposed controller. The convergence and signal tracking properties are better than the conventional sliding mode controller. Finally, we employed the experiment to validate the proposed method is feasible.

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Acknowledgments

This work is supported by the Ministry of Science and technology in Taiwan, Republic of China, through Grant MOST104-2221-E-224-019.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan

    Huann-Keng Chiang & Feng-Jui Hsu

  2. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, 64002, Taiwan

    Chun-Chiang Fang

Authors
  1. Huann-Keng Chiang
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  2. Chun-Chiang Fang
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  3. Feng-Jui Hsu
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Correspondence to Chun-Chiang Fang.

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Chiang, HK., Fang, CC. & Hsu, FJ. The neural sliding mode controller design of fan-plate system. Artif Life Robotics 21, 49–56 (2016). https://doi.org/10.1007/s10015-015-0252-7

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  • DOI: https://doi.org/10.1007/s10015-015-0252-7

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