电气传动2025，Vol.55Issue(2)：3-12,10.DOI:10.19457/j.1001-2095.dqcd25326
带有参数在线辨识的永磁同步电机模型预测控制研究
Model Predictive Control Study of Permanent Magnet Synchronous Motor with Parameters Online Identification
摘要
Abstract
Permanent magnet synchronous motor(PMSM)has the advantages of fast dynamic response,high power density,and high torque at low speed,but the temperature variation and complex working conditions will cause the variation of PMSM parameters,thus affect motor performance and reduce the output efficiency.To address the controller parameter mismatch problem caused by the change of motor parameters in the model predictive current control,firstly an adaptive linear(Adaline)neural network was used for the online identification of the parameters of the PMSM such as inductance,flux and resistance,and then the normalized least mean square(NLMS)algorithm was introduced to improve the Adaline neural network algorithm in order to improve the convergence speed and computational accuracy of the algorithm.In addition,the high-frequency current component of the model predictive control was utilized to calculate the PMSM rotor position and the parameters of rotor angle and speed were adopt to achieve sensorless control.The experimental results show that the improved NLMS-Adaline neural network is of practical value in terms of speed and accuracy compared with recursive RLS and traditional Adaline online identification,along with a nice adaptation to parameters mismatching.关键词
永磁同步电机/参数在线辨识/自适应线性神经网络/归一化/模型预测电流控制Key words
permanent magnet synchronous motor(PMSM)/parameter online identification/adaptive linear(Adaline)neural network/normalization/model predictive current control(MPCC)分类
信息技术与安全科学引用本文复制引用
徐海,米彦青,王艳阳,徐志鹏..带有参数在线辨识的永磁同步电机模型预测控制研究[J].电气传动,2025,55(2):3-12,10.基金项目
民航安全能力建设基金(AADSA2021017) （AADSA2021017）
