基于电机参数辨识的模型预测控制研究  

Research on Predictive Control Based on Motor Parameter Identification

作　　者：童锐 蒋全 

机构地区：[1]上海理工大学机械工程学院,上海

出　　处：《建模与仿真》2023年第4期4171-4182,共12页Modeling and Simulation

摘　　要：永磁同步电机的电气参数会随着工作负荷和环境的变换而发生变化。为了解决永磁同步电机模型预测控制中电机数学模型由于工况引起的参数不匹配问题,本文提出了一种基于电机参数在线辨识与模型预测控制相结合的控制系统。首先分析永磁同步电机电气参数的变化对预测控制产生的影响,结合对模型预测的简化确认对电感和磁链进行辨识。通过扩展卡尔曼滤波对电机参数进行在线辨识为预测控制提供准确的数学模型。通过MATLAB/Simulink仿真设计电感磁链参数的变化来检测在线辨识对电机数学模型的修正效果以达到优化控制的目的。The electrical parameters of permanent magnet synchronous motor will change with the change of working load and environment. In order to solve the problem of parameter mismatch caused by the operating conditions of the mathematical model of the permanent magnet synchronous motor in the model predictive control, this paper proposes a control system based on the combination of online identification of motor parameters and model predictive control. Firstly, the influence of the change of electrical parameters of permanent magnet synchronous motor on predictive control is analyzed, and the inductance and flux linkage are identified with the simplified confirmation of the predictive model. The extended Kalman filter is used for on-line identification of motor parameters to provide accurate mathematical model for predictive control. Through MATLAB/ Simulink simu-lation, the change of inductance flux parameters is designed to detect the correction effect of online identification on the mathematical model of the motor to achieve the purpose of optimal control.

关 键 词：模型预测控制 扩展卡尔曼滤波 电气参数 在线辨识 工作负荷 电机数学模型 优化控制 永磁同步电机 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]