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一种基于折息最小二乘法的PMSM磁链辨识方法

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永磁同步电机(permanent magnet synchronous motor,PMSM)的磁链准确辨识是实现高性能电机控制的基础.针对传统递推最小二乘(recursive least squares,RLS)法受噪声影响小但存在数据饱和,影响辨识精度和动态性问题,以及遗忘最小二乘(recursive least squares with forgetting factor,FRLS)法避免数据饱和但存在参数估计误差与动态跟踪性能矛盾的问题,文章提出一种基于折息最小二乘(recursive least squares with discount factor,DRLS)法的磁链辨识方法.该算法在FRLS法中引入加权因子构成折息因子,采用递推方法进行磁链辨识,减小参数估计误差,提高磁链辨识精度及动态跟踪能力.通过MATLAB仿真及半实物仿真试验,验证所提磁链识别方法的有效性.
A flux linkage identification method of PMSM based on recursive least squares with discount factor
The accurate flux linkage identification of permanent magnet synchronous motor(PMSM)is the basis of high performance motor control.Traditional recursive least squares(RLS)algorithm is less sensitive to noise,but there is a phenomenon of data saturation,which affects the identification precision and dynamics.RLS with forgetting factor(FRLS)algorithm can avoid data saturation,but it has the problem of contradiction between parameter estimation error and dynamic tracking perform-ance.In this paper,a flux linkage identification method of PMSM based on RLS with discount factor(DRLS)algorithm is put forward.The weighted factor is introduced into FRLS to form the discount factor,and the recursive method is used to identify the flux linkage,which reduces the parameter esti-mation error and improves the accuracy of flux linkage identification and the dynamic tracking ability.MATLAB simulation and hardware-in-the-loop experiments verify the effectiveness of the proposed method.

permanent magnet synchronous motor(PMSM)flux linkage identificationrecursive least squares(RLS)algorithmRLS with forgetting factor(FRLS)algorithmRLS with discount factor(DRLS)algorithm

谢明睿、赖纪东、苏建徽、周晨光、郑伟炜

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合肥工业大学 电气与自动化工程学院,安徽合肥 230009

永磁同步电机(PMSM) 磁链辨识 递推最小二乘(RLS)法 遗忘最小二乘(FRLS)法 折息最小二乘(DRLS)法

合肥工业大学产学研校企合作资助项目

W2020JSKF0281

2024

10.3969/j.issn.1003-5060.2024.08.007

合肥工业大学学报(自然科学版)
合肥工业大学

合肥工业大学学报(自然科学版)

CSTPCD北大核心
影响因子：0.608
ISSN：1003-5060
年,卷(期)：2024.47(8)
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