基于扩张状态观测器的永磁同步电机重复学习控制

Extended state observer-based repetitive learning control for permanent magnet synchronous motors

本文针对非参数不确定永磁同步电机系统,提出一种基于扩张状态观测器的重复学习控制方法,实现对周期期望轨迹的高精度跟踪.首先,将永磁同步电机中的非参数不确定性分为周期不确定与非周期不确定两部分.其次,构造包含周期不确定的未知期望控制输入,并设计重复学习律估计未知期望控制输入并补偿系统周期不确定.在此基础上,设计扩张状态观测器,估计系统未知状态和补偿非周期性不确定,进而提高系统鲁棒性.与已有的部分限幅学习律相比,本文提出的全限幅重复学习律可以保证估计值的连续性且能够被限制在指定的界内.最后,基于李雅普诺夫方法分析误差的收敛性能,并给出仿真和实验结果验证本文所提方法的有效性.

In this paper,an extended state observer-based repetitive learning control scheme is proposed for permanent magnet synchronous motors(PMSMs)with nonparametric uncertainties.First of all,the nonparametric system uncertainties of PMSMs are divided into two separated parts.Then,an unknown desired control input including the periodically uncertainties is constructed,and a repetitive learning law is presented to estimate the unknown desired control input and compensate for periodically uncertainties.On this basis,an extended state observer is designed to estimate the unknown system state and non-periodic uncertainties,such that the robustness of the whole system can be enhanced.Compared with the existing partially saturated learning law,the proposed full saturated learning law in this paper can ensure that the estimation is continuous and constrained within a prescribed region.Finally,the Lyapunov synthesis method is employed to analyze the error convergence performance,and simulation and experimental results are provided to illustrate the effectiveness of the proposed scheme.