基于双段卡尔曼滤波的永磁电机无传感器控制

Sensorless Control of Permanent Magnet Motor Based on Two-Stage Kalman Filtering

基于传统卡尔曼滤波的无传感器算法可以在一定程度上消除模型误差和测量误差对状态变量估计值的影响,但存在运算量大、硬件成本高的问题.为解决这一问题,在保持原有算法优点的基础上,文中将一种最优双段卡尔曼滤波算法引入到永磁电机无传感器控制技术中,在静止两相坐标系上建立相应的线性数学模型,取静止两相定子电流值和反电动势值分别作为全阶状态量和增广状态量,以此设计最优双段卡尔曼滤波算法.为提高估计的精度,利用软件锁相环结构得出所需的速度值.实验结果表明,该方法与传统的卡尔曼滤波器在数学上等效,但减少了24.49%的运算时间.

Although the conventional sensorless algorithm based on Kalman filtering （KF）can eliminate the impact of model error and measurement error on the estimated state variables to a certain extent,heavy computation load and high cost may simultaneously occur.In order to solve this problem and keep the advantages of the original algo-rithm,a novel sensorless control strategy based on the optimal two-stage Kalman filtering is proposed in stationary two-phase coordinates for permanent magnet synchronous motor.In this new strategy,stator currents in the stationa-ry reference frame are taken as the full-order states and the back-electromotive-force is taken as the augmented sys-tem state.In order to improve the parameter identification precision,software phase-locked loop is adopted in the control strategy to estimate the rotor speed.Experimental results show that the proposed strategy is mathematically equivalent to the traditional Kalman filtering,with an operation time reduction of 24.49%.