Model Predictive Control Method Based on Data-Driven Approach for Permanent Magnet Synchronous Motor Control System  

作　　者：LI Songyang CHEN Wenbo WAN Heng 李松阳;陈文博;万衡

机构地区：[1]School of Railway Transportation,Shanghai Institute of Technology,Shanghai 201418,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2025年第2期270-279,共10页上海交通大学学报(英文版)

摘　　要：Permanent magnet synchronous motor(PMSM)is widely used in alternating current servo systems as it provides high eficiency,high power density,and a wide speed regulation range.The servo system is placing higher demands on its control performance.The model predictive control(MPC)algorithm is emerging as a potential high-performance motor control algorithm due to its capability of handling multiple-input and multipleoutput variables and imposed constraints.For the MPC used in the PMSM control process,there is a nonlinear disturbance caused by the change of electromagnetic parameters or load disturbance that may lead to a mismatch between the nominal model and the controlled object,which causes the prediction error and thus affects the dynamic stability of the control system.This paper proposes a data-driven MPC strategy in which the historical data in an appropriate range are utilized to eliminate the impact of parameter mismatch and further improve the control performance.The stability of the proposed algorithm is proved as the simulation demonstrates the feasibility.Compared with the classical MPC strategy,the superiority of the algorithm has also been verified.

关 键 词：permanent magnet synchronous motor(PMSM) model predictive control(MPC) data-driven model predictive control(DDMPC) 

分 类 号：TM351[电气工程—电机]