基于改进灰狼优化算法的PMSM滑模自抗扰控制

Sliding mode active disturbance rejection control for PMSM based on improved grey wolf optimization algorithm

针对永磁同步电动机(PMSM)伺服系统在受外部扰动、参数变化等不确定性因素影响下系统鲁棒性变差的问题,提出一种基于改进灰狼优化算法的永磁同步电动机滑模自抗扰控制方案。首先,对传统扩张状态观测器存在的峰值问题而降低不确定性的观测精确度,设计了变增益扩张状态观测器,通过降低初始增益来提高观测精确度。其次,用滑模控制器代替传统自抗扰控制中的非线性误差状态反馈环节,进而提高系统的鲁棒性。由于参数比较多不易调节,利用改进灰狼优化算法对参数进行优化,充分发挥控制器的性能。基于Lyapunov理论分析了该方案的稳定性。通过系统仿真实验结果表明,该方法能够有效跟踪速度给定,克服参数变化、负载扰动等不确定性因素的影响,保证了PMSM伺服系统的强鲁棒性。

Aiming at the problem that the robustness of permanent magnet synchronous motor(PMSM)servo system becomes worse under the influence of uncertainties,such as external disturbances and parameter changes,a sliding mode active disturbance rejection control scheme based on improved gray wolf optimization algorithm for PMSM was proposed.Firstly,the traditional extended state observer has a peak problem,which reduces the observation accuracy of uncertainty.Therefore,a variable gain extended state observer was designed,which improves the observation accuracy by reducing the initial gain.Secondly,sliding mode control was used to replace the traditional nonlinear state error feedback in active disturbance rejection control to improve the robustness of the system.Because there are many parameters and it is difficult to adjust,the improved gray wolf optimization algorithm was used to optimize the parameters and given full play to the performance of the controller.The stability of the scheme was analyzed based on Lyapunov theory.The system simulation results show that this method can effectively track the given speed,overcome the influence of uncertainty factors such as parameter changes and load disturbances,and ensure the strong robustness of PMSM servo system.