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.

本文提出了一种基于极限学习机的超扭曲重复控制（ELMSTRC），以提高抖动少的周期性信号的跟踪精度。所提出的算法对于由质量和粘性摩擦变化引起的设备不确定性具有鲁棒性。此外，它通过使用基于极限学习机的超扭曲算法来补偿非线性摩擦和反冲。首先，设计重复控制来跟踪周期性参考并补偿粘性摩擦。然后，构建基于稳定的极限学习机的超扭曲控制，以补偿非周期性扰动，非线性摩擦，齿隙和设备不确定性。根据Lyapunov稳定性准则分析了ELMSTRC系统的稳定性。所提出的算法在具有不同负载，间隙和摩擦条件的无刷直流伺服电机上得到了验证。仿真和实验比较突出了该算法的优势。