基于MRAS算法的永磁同步电动机无传感器自适应预测控制  

Sensorless Adaptive Predictive Control of PMSM Based on MRAS Algorithm

作　　者：宋搏洋 SONG Bo-yang(Shenyang Electric Driving Research Institute(Co.Ltd.),Shenyang 110141,China)

机构地区：[1]沈阳电气传动研究所(有限公司),辽宁沈阳110141

出　　处：《电气开关》2022年第2期43-48,51,共7页Electric Switchgear

摘　　要：为了保证传统形式的模型参考自适应方法(Model Reference Adaptive System, MRAS)在永磁同步电动机(Permanent Magnet Synchronous Motor, PMSM)的控制过程中保持良好的动态性能和快速的响应策略,本文提出一种基于MRAS经典策略的综合算法模型。此方法使用扩展卡尔曼滤波(Extended Kalman Filter, EKF)技术来代替MRAS传统形式的自适应律,同时综合了一种线性的神经网络(Back-Propagation Network, BPN),通过逆向传播策略对MRAS自适应律结果进行优化补偿,扩展了卡尔曼滤波算法对PMSM辨识目标的估算范围;最后通过MATLAB/Simulink仿真环境对二者进行了仿真结果对比。结果表明,与经典的MRAS算法相比,改进之后的MRAS综合算法模型在无传感器控制过程中,对PMSM控制参数采集的准确性和动态响应过程均有所提升,进一步证明了MRAS综合算法模型在永磁同步电动机拖动控制系统中的应用价值。In order to ensure the traditional form of Model Reference Adaptive System(MRAS) to maintain good dynamic performance and fast response strategy in the control process of Permanent Magnet Synchronous Motor(PMSM),this paper proposes a comprehensive algorithm model based on the classic strategy of MRAS.This method uses extended Kalman filter(EKF)technology to replace the traditional adaptive law of MRAS.At the same time, a linear neural network(BPN)is integrated to optimize and compensate the results of MRAS adaptive law through reverse propagation strategy, which expands the estimation range of Kalman filter algorithm for PMSM identification target.Finally, the simulation results are compared by MATLAB/Simulink simulation environment.The results show that compared with the classical MRAS algorithm, the improved MRAS integrated algorithm model improves the accuracy of PMSM control parameter acquisition and dynamic response process in the process of sensorless control, which further proves the application value of MRAS integrated algorithm model in the drive control system of permanent magnet synchronous electric machine.

关 键 词：永磁同步电动机 模型参考自适应 扩展卡尔曼滤波 BP网络 

分 类 号：TM351[电气工程—电机]