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Perturbation wavelet neural sliding mode position control for a voice coil motor driver

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Abstract

To cope with the nonlinear electro-magneto-mechanical characteristics, this paper proposes a perturbation wavelet neural sliding mode position control (PWSPC) system for a voice coil motor (VCM) driver. A perturbed wavelet neural network (PWNN) approximator is used to online approximate the unknown nonlinear term in the VCM system dynamics. The PWNN approximator uses perturbed wavelet functions to handle the rules uncertainties like interval type-2 fuzzy sets. The structure learning ability enables the PWNN approximator to evolve its structure online. Further, the parameter learning laws and stability analysis are derived in the sense of Lyapunov function; thus, the parameter convergence and system stability can be guaranteed. Finally, the experimental results verify that the proposed PWSPC system can achieve favorable control performance such as good disturbance rejection and good tracking accuracy.

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Acknowledgements

The authors are grateful to the associate editor and the reviewers for their valuable comments. The study was funded by the Ministry of Science and Technology of Republic of China under Grant MOST 103-2221-E-032-063-MY2 and MOST 105-2628-E-032-001-MY3.

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Authors and Affiliations

  1. Department of Electrical Engineering, Tamkang University, No. 151, Yingzhuan Rd., Tamsui Dist, New Taipei City, 25137, Taiwan

    Chun-Fei Hsu & Wei-Fu Kao

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  1. Chun-Fei Hsu
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  2. Wei-Fu Kao
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Correspondence to Chun-Fei Hsu.

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I declare that I have no financial and personal relationships with other people or organizations that can inappropriately influence our work; there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled “Perturbation wavelet neural sliding mode position control for a voice coil motor driver.”

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Hsu, CF., Kao, WF. Perturbation wavelet neural sliding mode position control for a voice coil motor driver. Neural Comput & Applic 31, 5975–5988 (2019). https://doi.org/10.1007/s00521-018-3413-5

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  • DOI: https://doi.org/10.1007/s00521-018-3413-5

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