全文下载：2013010080
逄海萍， 孙宁宁， 章平
（青岛科技大学 自动化与电子工程学院，山东 青岛 266042）
摘要: 针对永磁同步电动机（permanent magnet synchronous motor, PMSM）研究其在外界作用发生突变情况下的状态观测问题。首先基于理想状态下PMSM的数学模型，采用强跟踪滤波器（the strong tracking filter, STF）算法估计电动机的转速、转角、磁链和转矩，并通过仿真比较STF算法与扩展卡尔曼滤波器（extended Kalman filter, EKF）算法的估计性能，结果表明STF与EKF相比对突加给定起动以及突加扰动等因素引起的状态快速变化能够实现更快速、更准确的跟踪。最后在考虑电动机的铁耗情况下分别通过这两种方法进行状态观测，所得结果与上述结论相符，进一步表明将STF应用于实际电动机进行状态观测的可行性和优越性。
关键词: 永磁同步电机； 强跟踪滤波器； 扩展卡尔曼滤波器； 状态观测； 无速度传感器
中图分类号: TP 273文献标志码： A
State Observation for Permanent Magnet Synchronous Motors Based on Strong Tracking Filter
PANG Hai-ping, SUN Ning-ning, ZHANG Ping
(CollegeofAutomationand Electronic Engineering,QingdaoUniversityof Science and Technology,Qingdao266042,China)
Abstract: The problem of state observation for permanent magnet synchronous motors (PMSMs) subject to suddenly changed external effects is studied in this paper. Firstly, based on the PMSM mathematical model in the ideal situation, the motor states include speed, position, flux linkage and torque are estimated by adopting the strong tracking filter (STF) algorithm. Then, the estimation performance of STF algorithm and the extended Kalman filter (EKF) algorithm are compared by simulation. The results show that compared to EKF, STF can achieve faster and more accurate tracking performance for the state rapid changes that caused by sudden external disturbances or starting with sudden given control effect. At the last, the two kinds of algorithm are both used to estimate the states considering the motor’s core loss，and the results are consistent with the conclusions above，which further show the feasibility and superiority of applying the STF algorithm to estimating states in practice.
Key words: PMSM; STF; EKF; state observation; speed-sensorless
收稿日期：2012-04-19
基金项目： 国家自然科学基金项目（60940018; 61104004）；山东省自然科学基金项目（ZR2011FQ006）
作者简介： 逄海萍（1964—），女，教授.
