To solve the complexity and non-real-time problems that exist in extended Kalman filter(EKF) for permanent magnet synchronous motors ( PMSM) , a new kind of algorithm based on reduced - order extended Kalman filter ( KEKF) was developed in this paper aiming to predict the positions and speeds of rotors and to simplify the structure of the filter by defining state variables appropriately. Simulation results show that both of the estimated rotor position and speed can better track the real value,which maintains the advantages of the conventional EKF. Simultaneously, the lower ordered algorithm significantly reduces the computations, enhances its real-time processing and it is easy to realize digitization.%针对永磁同步电动机扩展卡尔曼滤波(EKF)算法复杂、不利于实时性等问题,提出了一种基于降阶扩展卡尔曼滤波器(REKF)来预估转子位置和速度的新算法,通过对状态变量的适当选取,简化了滤波器结构.仿真结果表明:估算的转子位置和速度均能较好地跟踪实际值,继承了传统扩展卡尔曼滤波的优点.同时,算法阶数的降低显著减少了运算量,实时性得到增强,更易于数字化实现.
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