一种改进EKF的PMSM无传感器控制策略

摘要

In the sensorless control system of permanent magnet synchronous motor-PMSM based on extended Kalman filter-EKF,the change of environmental temperature will affected the parameter of PMSM,but the gain of standard EKF is not self-adapting,especially for the mutation of state variable,which may arouse mistake in the estimation.A kind of suboptimal multiple fading extend Kalman filter-SMFEKF was used to estimate the speed and position of rotor.The result of simulation shows that this method can improve the dynamic performance of EKF,and it also has higher estimation precision and real time performance,better robustness,and is suitable for frequent starting,braking and other working applications.%在无传感器的永磁同步电动机控制系统中,环境温度的变化会引起电机的参数(如电感、电阻等)的变化,扩展卡尔曼滤波器(EKF)的增益矩阵并不能自适应调整跟踪变化,特别是突变的状态变量的变化,导致状态估计不准.针对稳态时跟踪能力变差的问题,引入一种带多重次优渐消因子的扩展卡尔曼滤波器(SMFEKF)来估算转子速度和位置,仿真结果表明,该算法能改善扩展卡尔曼滤波器的动态跟踪能力,具有精度高、实时性强,鲁棒性好,适合于频繁起动、制动等工作应用场合.