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Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach

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Abstract

This paper presents an improved equivalent-input-disturbance (EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference input. The improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality (LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization (PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator (LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others.

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Acknowledgements

This work was supported in part by National Key R&D Program of China (Grant No. 2017YFB1300900), National Natural Science Foundation of China (Grant No. 61873348), Natural Science Foundation of Hubei Province, China, (Grant No. 2020CFA031), Wuhan Applied Foundational Frontier Project (Grant No. 2020010601012175), and the 111 Project (Grant No. B17040).

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Authors and Affiliations

  1. School of Automation, China University of Geosciences, Wuhan, 430074, China

    Qicheng Mei, Zhentao Liu & Min Wu

  2. Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China

    Qicheng Mei, Zhentao Liu & Min Wu

  3. School of Engineering, Tokyo University of Technology, Hachioji, 192-0982, Japan

    Jinhua She

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  1. Qicheng Mei
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  2. Jinhua She
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  3. Zhentao Liu
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  4. Min Wu
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Correspondence to Jinhua She.

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Mei, Q., She, J., Liu, Z. et al. Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach. Sci. China Inf. Sci. 65, 182205 (2022). https://doi.org/10.1007/s11432-020-3192-5

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  • DOI: https://doi.org/10.1007/s11432-020-3192-5

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