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

被引：0
作者：
Raymond Chuei
Zhenwei Cao
机构：
[1] Swinburne University of Technology,Faculty of Science, Engineering and Technology
来源：
Neural Computing and Applications | 2020年 / 32卷
关键词：
Aperiodic disturbance; Backlash; Chattering; Coulomb friction; Extreme learning machine; Neural network; Nonlinear control; Parameter uncertainty; Periodic signal; Repetitive control; Super-twisting control; Viscous friction;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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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页码：14483 / 14495
页数：12
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