基于 MEKF 的直流无刷电机磁极位置与转速检测技术

传统的扩展卡尔曼滤波器(Extended Kalman Filter,EKF)用于无刷直流电机状态辨识时,观测数据容易出现残差,辨识结果偏差大,位置及转速存在耦合,导致辨识系统鲁棒性弱。文章基于离散的直流无刷电机(Brushless DC Moter,BLDCM)数学模型和M-估计方法,构建了改进的扩展卡尔曼滤波算法(MEKF)。首先,基于BLDCM的工作原理,建立了独立于EKF的BLDCM换相离散模型;其次,通过修正系统观测矩阵,对转速与位置的强耦合关系进行解耦,实现了EKF分离变量辨识;最后,基于去耦合后的时序模型设计出独立于EKF的转子位置检测模块,无需深度滤波就可实现转子的精确定位。实验仿真结果表明,文章方法能够有效抑制卡尔曼滤波器的粗差扰动,提高了系统抵抗初始值不确定性的干扰和系统鲁棒性。

Detection Technology of Rotor Position and Speed of BLDCM Based on MEKF

The traditional Extended Kalman Filter (EKF) will lead to large errors of estimation data and robustness weakening of the identification system when applied to brushless DC motor (BLDCM) to estimate rotor position and speed simultaneously. In this paper, based on the discrete mathematical model of BLDCM and M-estimation, an improved EKF(MEKF) was proposed. Firstly, based on the commutation principle of the BLDCM operation and EKF model, a separate commutation model was built up, which was independent to the EKF. Secondly, in order to identify the rotor speed and position with more precision and to enhance the robustness of system, the observation matrix was modified by M-estimation, and a decoupling technology of speed and position was adopted in the system correspondingly. Thirdly, based on the decoupled time series model of the motor, a rotor position detection model was designed out, so that a precision rotor position can be realized in practice without deep filtration which will lead to great lagging. The experiment and simulationresults show that this method can effectively abate the errors disturbance of the EKF, and it also significantly enhances the anti-interference of the initial value and robustness.