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Extreme learning machine-based super-twisting repetitive control for aperiodic disturbance, parameter uncertainty, friction, and backlash compensations of a brushless DC servo motor

  • S.I. : Extreme Learning Machine and Deep Learning Networks
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Abstract

This paper presents an extreme learning machine-based super-twisting repetitive control (ELMSTRC) to improve the tracking accuracy of periodic signal with less chattering. The proposed algorithm is robust against the plant uncertainties caused by mass and viscous friction variations. Moreover, it compensates the nonlinear friction and the backlash by using extreme learning machine based super-twisting algorithm. Firstly, a repetitive control is designed to track the periodic reference and compensate the viscous friction. Then, a stable extreme learning machine-based super-twisting control is constructed to compensate the aperiodic disturbance, nonlinear friction, backlash and plant uncertainties. The stability of ELMSTRC system is analysed based on Lyapunov stability criteria. The proposed algorithm is verified on a brushless DC servo motor with various loading, backlash and friction conditions. The simulation and experimental comparisons highlight the advantages of the proposed algorithm.

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  1. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia

    Raymond Chuei & Zhenwei Cao

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  1. Raymond Chuei
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  2. Zhenwei Cao
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Correspondence to Raymond Chuei.

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Chuei, R., Cao, Z. Extreme learning machine-based super-twisting repetitive control for aperiodic disturbance, parameter uncertainty, friction, and backlash compensations of a brushless DC servo motor. Neural Comput & Applic 32, 14483–14495 (2020). https://doi.org/10.1007/s00521-020-04965-w

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  • DOI: https://doi.org/10.1007/s00521-020-04965-w

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