基于自适应插值扩展卡尔曼滤波的永磁同步电机状态估计

PMSM无传感控制的转速与转子位置估计对系统至关重要,扩展EKF算法作为无传感控制技术被广泛应用在工业领域。但是EKF算法在系统线性化过程中产生截断误差,对于高度非线性模型无法得到精确估计值。为减小EKF算法因非线性问题而造成的误差,文中提出了一种基于AIEKF状态估计法。该方法以量化状态方程的非线性程度为依据,通过添加伪状态值减小EKF算法线性化中产生的误差对估值精度的影响,从而降低系统在线性化过程引起的误差。仿真计算结果表明,AIEKF较EKF的截断误差平均降低了55.6%。

State Estimation for Permanent Magnet Synchronous Motor Based on Adaptive Interpolation Extended Kalman Filter

Speed and rotor position estimation are critical for PMSM non-sensing control system. The EKF algorithm has been widely used in the industrial field as a sensorless control technology. However, the EKF algorithm produces truncation errors during system linearization, and accurate estimates cannot be obtained for highly nonlinear models. In order to reduce the error caused by nonlinear problem of EKF algorithm, based on AIEKF, a state estimation for PMSM was proposed in the study. Based on the degree of nonlinearity of the quantized state equation, this method reduced the influence of the error generated in the linearization of EKF algorithm on the estimation accuracy by adding pseudo state value, thereby reducing the errors caused by the linearization process of the system. Finally, simulation showed that the interception error of AIEKW was lower than EKF by 55.6%.