基于SRMA-UKF的永磁同步电机无传感器系统设计

SRMA-UKF-based Design of the Sensorless System for Permanent Magnet Synchronous Motors

为了解决传统UKF在永磁同步电机无传感器系统中存在的鲁棒性差、由于舍入误差导致的协方差矩阵发散的问题,提出了带遗忘因子的平方根UKF算法,在滤波过程中采用平方根矩阵代替协方差矩阵进入迭代运算,有效的克服了系统的发散问题,并且通过引入了遗忘因子的概念,将原有滤波器改造成强跟踪滤波器,从而提高了系统的鲁棒性。从MATLAB/Simulink仿真结果可以发现,与UKF、SR-UKF相比,对于突变状态的跟踪能力,SRMA-UKF有较大的提高,转子速度以及转子位置跟踪更精确,误差更小,鲁棒性得到提高。

To overcome shortages of traditional UKF in the sensorless system of the permanent magnet synchronous motor （ PMSM ） , such as poor robustness and covariance matrix divergence caused by rounding error, this paper presents an algorithm of square root unscented Kalman filter （UKF} with forgetting factor. In the filtering process, square root matrix, in place of covariance matrix, is used in iterative computation, thus overcoming the divergence problem of the system effectively. Furthermore, through introduction of the concept of forgetting factor, the original filter is changed into a strong tracking filter, so as to improve the robustness of the system. It can be seen from the result of MATLAB/Simulink that as compared with UKF and SR-UKF, SRMA-UKF has achieved much improvement of mutation-tracking ability. The rotor speed and position tracking becomes more accurate with less error and better robustness.