永磁直线同步电动机无位置传感器控制系统的研究  

Research on Permanent Magnet Linear Synchronous Motor Position Sensorless Control System

作　　者：余佩琼[1] 陆亿红[1] 王涌[1] 杨伟民[2] 陈子辰[2] 

机构地区：[1]浙江工业大学信息工程学院,浙江省杭州市310032 [2]浙江大学现代制造工程研究所,浙江省杭州市310027

出　　处：《中国电机工程学报》2007年第24期53-57,共5页Proceedings of the CSEE

基　　金：国家自然科学基金项目(50475101);浙江省自然科学基金项目(Y104193)~~

摘　　要：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。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.

关 键 词：非线性滤波 UNSCENTED卡尔曼滤波 永磁直线同 步电动机 无位置传感器 

分 类 号：TP271.4[自动化与计算机技术—检测技术与自动化装置]