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Fuzzy Broad Learning Adaptive Control for Voice Coil Motor Drivers

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Abstract

The position of a voice coil motor (VCM) driver is difficult to control in a stable and highly precise manner. To address these challenges, this study proposes a fuzzy broad learning adaptive control (FBLAC) system consisting of a fuzzy broad controller and a robust controller. The fuzzy broad controller uses a fuzzy broad-learning system (FBLS) to approximate an ideal controller online, and the robust controller is designed to keep the system stable. The gradient descent method and the chain rule are applied to adjust all the FBLS parameters online to increase its approximation and learning capacities. Furthermore, the experimental results demonstrate that the proposed FBLAC system has good tracking performance and uncertainty rejection properties. The main contributions of this study are as follows: (1) An FBLS with a simple structure and full-tuned parameter learning laws to improve its learning ability was investigated. (2) Stability analysis of the closed-loop FBLAC system was proved based on the gradient descent method and the Lyapunov stability theorem. (3) Several experimental evaluations and analyses were conducted to demonstrate the effectiveness of the proposed FBLAC method.

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Acknowledgements

The authors are grateful to the associate editor and the reviewers for their valuable comments. This study was funded by the Ministry of Science and Technology of Republic of China under Grant MOST 110-2221-E-032-038-MY2.

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Authors and Affiliations

  1. Department of Electrical Engineering, Tamkang University, New Taipei City, Taiwan

    Chun-Fei Hsu

  2. Department of Electrical and Computer Engineering Institute of Electrical and Control Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Bo-Rui Chen & Bing-Fei Wu

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  1. Chun-Fei Hsu
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  2. Bo-Rui Chen
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  3. Bing-Fei Wu
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Correspondence to Chun-Fei Hsu.

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Hsu, CF., Chen, BR. & Wu, BF. Fuzzy Broad Learning Adaptive Control for Voice Coil Motor Drivers. Int. J. Fuzzy Syst. 24, 1696–1707 (2022). https://doi.org/10.1007/s40815-021-01227-2

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  • DOI: https://doi.org/10.1007/s40815-021-01227-2

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