改进SADE-EKF的永磁同步电机参数辨识

针对永磁同步电机（PMSM）参数辨识中扩展卡尔曼滤波（EKF）难以确定合适的系统噪声矩阵Q和量测噪声矩阵R的问题，提出了一种改进自适应差分进化算法（SADE）-EKF的PMSM参数辨识方法。首先分析了扩展卡尔曼滤波器的工作原理，建立了双线程辨识模型；然后通过改进差分进化算法（DE）的变异策略跳出局部最优，并设计了合适的适应度函数；最后，通过SADE算法对EKF的Q和R进行优化。实验结果表明，改进的SADE-EKF在辨识电机参数时比传统的EKF具有更好的收敛速度和辨识精度。

PMSM Parameter Identification Method Based on an Improved SADE-EKF

In view of the deficiency of the extended Kalman filter (EKF) in determining the appropriate system noise matrix Q and measurement noise matrix R in parameter identification of permanent magnet synchronous motor (PMSM), a PMSM parameter identification method has thus been proposed based on improved self adaptive differential evolution (SADE) -EKF. Firstly, based on an analysis of the working principle of the extended Kalman filter, the double thread identification model has thus been established. Then, the mutation strategy of the improved differential evolution algorithm (DE) is adopted to jump out of the local optimum, followed by a design of a suitable fitness function. Finally, the Q and R of EKF are optimized by using Sade algorithm. The experimental results show that the improved SADE-EKF is characterized with a better convergence speed and identification accuracy than the traditional EKF in identifying motor parameters.