一种基于折息最小二乘法的 PMSM 磁链辨识方法

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。

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 performance. 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 estimation 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.