永磁直线同步电动机无位置传感器控制系统的研究

永磁直线同步电动机直接驱动系统的无位置传感器控制中，需要实现电机的位置及速度估计。针对直线电机直接驱动系统具有强非线性，将一种新的滤波方法--Unscented卡尔曼滤波(UKF)应用于直线电机无位置传感器驱动系统的非线性状态估计中。UKF采用确定性采样策略,通过UT变换实现状态均值和方差的非线性传播，避免了扩展卡尔曼滤波(EKF)产生的线性化误差，并且无需计算雅可比矩阵。同时，采用Cholesky因式分解等方法保证滤波递推过程中协方差矩阵的半正定性，有效地避免滤波的发散，提高算法的计算精度。数值仿真及实验结果表明，所给出的算法是可行而有效的。

Research on Permanent Magnet Linear Synchronous Motor Position Sensorless Control System

For the position sensorless control of Permanent magnet linear synchronous motor(PMLSM) direct drive system, it is necessary to estimate the information of speed and position online. Due to the nonlinear of direct drive system, a novel states estimation approach-unscented Kalman filter(UKF) is applied in the nonlinear states estimation for the sensorless control of PMLSM. The UKF utilize a deterministic sampling approach to calculate mean and covariance term. The nonlinear transformations called the unscented transformation is applied to propagate the means and covariances of states, and the errors caused by linearization of extended Kalman filter(EKF) is eliminated. Furthermore, the Cholesky decomposition is used, rendering the state covariance with position semi-definiteness. The effectiveness of the proposed estimator is confirmed by the digital simulations results and experimental results.