摘要
针对传统扩展卡尔曼滤波(EKF)估计永磁同步电机(PMSM)速度、位置存在模型不精确,噪声不确定时估计精度不高、实时性差,且有可能导致滤波发散的问题,采用一种基于Sage-Husa的自适应渐消扩展卡尔曼滤波(AFEKF)算法。为了提高模型误差鲁棒性、减少超调,采用一种新型的基于指数趋近律的滑模转速控制器(SMC)。实验结果表明:所提出的控制策略相较于传统的比例积分(PI)和EKF算法能准确估计转速和位置。在启动时能较快到达预定速度,且无超调现象,迅速达到稳定状态;在加载后最大偏差较传统算法减小了1. 77%,且稳定状态下,转速误差下降了0. 371%,稳态位置误差减小了0. 45%。证实所提算法在PMSM无传感器控制系统中稳定性强,具有更好的实用性。
Aiming at problem that while traditional extended Kalman filter(EKF)is used to estimate velocity and position of permanent magnet synchronous motor(PMSM),the model is not accurate,the estimation precision is not high and the real-time performance is poor when the noise is uncertain,and it may lead to filtering divergence,a Sage-Husa adaptive fading extended Kalman filtering(AFEKF)algorithm is adopted.In order to improve the robustness of model errors and reduce overshoot,a new type of sliding-mode speed controller(SMC)based on exponential reaching law is adopted.The experimental result shows that,the proposed control strategy can estimate the rotating speed and position accurately compared to the traditional proportional integral(PI)and EKF algorithms.At the start time,it can reach the preset speed quickly,and has no overshoot phenomenon,and quickly reaches the stable state;the maximum deviation after loading is reduced by 1.77%compared with traditional algorithm,and the rotating speed error is decreased by 0.371%in steady state,and the steady-state position error is decreased by 0.45%.It is proved that the algorithm is stable and practical in PMSM sensorless control system.
作者
朱军
吴宇航
孟祥宾
李紫豪
ZHU Jun;WU Yu-hang;MENG Xiang-bin;LI Zi-hao(School of Electrical Engineering and Automation,Henan Polytechnic University,Jiaozuo 454000,China)
出处
《传感器与微系统》
CSCD
2019年第1期97-100,103,共5页
Transducer and Microsystem Technologies
基金
河南省重点研发与推广专项科技攻关项目
河南省高校基本科研业务费专项资金资助项目(NSFRF140115)
河南省教育厅科学技术重点研究项目(12A470004)
关键词
永磁同步电机
无传感器控制
滑模控制
自适应扩展卡尔曼滤波
permanent magnet synchronous motor (PMSM)
sensorless control
sliding-mode control
adaptive extended Kalman filtering(AEKF)
