扩展卡尔曼滤波结合前馈补偿永磁同步电机位置估计

Extended Kalman filters combined with feed-forward compensation for permanent magnet synchronous moter position estimation

中文关键词  同步电机  扩展卡尔曼滤波  遗传算法  负载转矩观测器

英文关键词  synchronous motors  extended Kalman filters  generic algorithms  load torque observer

中文摘要

      转速和转子位置的精确估计对建立永磁同步电机(permanent magnet synchronous moter, PMSM)转速、电流 双闭环矢量控制系统非常重要. 本文主要研究扩展卡尔曼滤波算法(extended Kalman filter, EKF)估计转速、转子位 置问题. 与传统EKF估计转子位置方法不同的是, 本文采用遗传算法(GA)优化EKF的协方差矩阵, 并给出P, Q, R矩 阵选取过程. 另外将负载转矩观测器观测的负载转矩同速度调节器的输出一起作为电流调节器的控制变量. 仿真 及实验结果表明: 文中提出的新方法有效缩短系统协方差参数选取时间, 提高转速的辨识精度和抗负载扰动能力.

英文摘要

      Accurate estimation of speed and rotor position plays an important role in the permanent magnet synchronous motor (PMSM) speed and current double closed-loop vector control system. This paper mainly discusses the extended Kalman filter (EKF) algorithm, which is used to estimate rotor speed and position problem. Unlike the traditional EKF method, the Generic algorithm (GA) is adopted to optimize the selection process of EKF covariances and the P, Q, R matrix selection progress is given. In addition, the load torque obtained by the load torque observer is used as the input, together with the speed regulator output, of the current regulator control variables. Simulation and experiment results show that, the proposed new strategy can shorten the system covariance parameters selection time, increase the tracking speed precision and embrace better anti disturbance ability.