基于WOA-EKF和MPC的永磁同步电机无位置传感器矢量控制
A Sensorless Vector Control of Permanent Magnet Synchronous Motor Based on WOA-EKF and MPC
崔鹏龙 1徐善永1
作者信息
- 1. 安徽理工大学 电气与信息工程学院,安徽 淮南 232001
- 折叠
摘要
针对扩展卡尔曼滤波器(EKF)算法难以获取理想的噪声协方差矩阵和EKF存在的延迟效应使得系统鲁棒性降低的问题,提出了一种基于WOA-EKF和MPC的永磁同步电机无位置传感器矢量控制方案.采用EKF实现永磁同步电机的无位置传感器矢量控制系统;采用鲸鱼优化算法(WOA)对EKF中的噪声协方差矩阵进行寻优;采用MPC控制器替换传统的PI速度控制器,并引入Luenberger负载观测器作为前馈补偿.仿真结果表明:经过优化后的EKF使得转速响应平稳、快速;MPC配合Luenberger观测器的方案相较于PI控制器,具有动态响应快、超调量小、鲁棒性强等优点.
Abstract
Aiming at the problem that the extended Kalman filter(EKF)in the control system of permanent magnet synchronous motor is difficult to obtain the ideal noise covariance matrix and the delay effect of the EKF algorithm,which reduces the robustness of the system,a sensorless vector control scheme of permanent magnet synchronous motor based on WOA-EKF and MPC is proposed.Firstly,the sensorless vector control system of per-manent magnet synchronous motor is realized by using EKF.Secondly,the whale optimization algorithm(WOA)is used to optimize the noise covariance matrix in EKF.Finally,the PI speed controller is replaced by an MPC controller,and a Luenberger load observer is introduced as feedforward compensation.The simulation results show that the optimized EKF makes the speed response smooth and fast.Compared with the traditional PI controller,the scheme of MPC with Luenberger observer has the advantages of fast dynamic response,small overshoot and strong robustness.
关键词
永磁同步电机/扩展卡尔曼滤波器/鲸鱼优化算法/模型预测控制/Luenberger观测器Key words
permanent magnet synchronous motor/extended Kalman filter/whale optimization algorithm/model predictive control/Luenberger observer引用本文复制引用
基金项目
国家自然科学基金(61772033)
安徽理工大学环境友好材料与职业健康研究与发展基金(ALW2021YF03)
出版年
2024
NETL
NSTL
万方数据