基于UKF算法的异步电机无速度传感器矢量控制系统

Speed Sensorless Vector Control of Asynchronous Motor Based on UKF

针对异步电机无速度传感器矢量控制系统的转速、磁链估计问题,提出一种基于无轨迹卡尔曼滤波(UKF)的状态估计算法。为避免常用的对称采样方法仅适用于处理高斯噪声并且sigma点多带来的计算量大问题,依据UT变换理论,采用最小偏度单形采样方法,减少了近1/3的计算量。仿真结果表明,所提出的基于最小偏度采样的UKF估计器能够准确地估计转子磁链、转速。

Concerning the speed and flux estimation problem of speed sensorless vector control of asynchronous motor, a state estimation algorithm based on Unscented Kalman Filter is proposed. Symmetrical sampling strategy is common used but not showed good performance while dealing with non-Gaussian noise, and the large number of sigma points causes reasonable computation load. In the paper, minimal skew simplex sampling is adopted according to UT transformation theory, as a result, nearly 1/3 computation load is reduced. Simulation results show that the UKF estimator based on the minimal skew simplex sampling can estimate flux and rotor speed precisely.