基于AEKF的永磁直线同步电机速度和位置估计算法  

Speed and Position Estimation Algorithm of Permanent Magnet Linear Synchronous Motor Based on Augmented Extended Kalman Filter

作　　者：陆华才[1] 徐月同[2] 

机构地区：[1]安徽省电气传动与控制重点实验室(安徽工程科技学院),安徽省芜湖市241000 [2]浙江省先进制造技术重点研究实验室(浙江大学),浙江省杭州市310027

出　　处：《中国电机工程学报》2009年第33期90-94,共5页Proceedings of the CSEE

基　　金：国家自然科学基金项目(50475101)~~

摘　　要：为实现永磁直线同步电机(permanent magnet linear synchronous motor,PMLSM)进给系统的无位置传感器控制,必须估计出电机的速度和磁极位置。提出一种通过电机端电流和端电压估计PMLSM速度和位置的方法——状态增广的扩展卡尔曼滤波(state augmented extended Kalman filter,AEKF)估计方法。该方法将电阻参数作为状态变量,通过扩展卡尔曼滤波(extended Kalman filter,EKF)估计方法对电机速度、磁极位置和电阻值同时进行估计,从而降低了电阻参数对估计结果的影响。实验表明,AEKF估计方法可以准确地估计电机速度和位置,基于AEKF的PMLSM进给系统无位置传感器控制具有良好的动态响应特性。In order to achieve position sensorless control for permanent magnet linear synchronous motor （PMLSM） drive system, speed and position of the motor must be estimated. A novel sensorless position and speed estimation algorithm was designed for PMLSM drive by measuring terminal voltages and currents. That was state augmented extended Kalman filter （AEKF） estimation method. The resistance of the motor was augmented to the state variable. Then, the speed, position and the resistance were estimated simultaneously through extended Kalman filter （EKF）. The influence of the resistance on the state estimation results could be reduced. As well as giving a detailed explanation of the new algorithm, experimental results were presented. It shows that the AEKF is capable of estimating system states accurately and reliability, and the proposed sensorless control system has a good dynamic response.

关 键 词：永磁直线同步电机 无位置传感器控制 增广型扩展卡尔曼滤波 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]