摘 要：针对永磁同步电机参数辨识中存在的多变量耦合、非线性强、传统方法精度不足的问题，本文提出一种多策略改进灰狼优化算法。该算法采用混合种群初始化策略提升初始解多样性，运用动态领导狼机制强化全局搜索，引入速度方向感知策略动态优化狼群移动轨迹，并融合个体历史最优与全局历史最优双重引导策略，以增强全局寻优能力。仿真结果表明，相比传统灰狼优化算法，本文所提策略可有效提升算法的参数辨识精度。
关键词：永磁同步电机；在线参数辨识；电机控制；灰狼优化算法
中图分类号：TM341；TP273 文献标志码：A DOI：10.15917/j.cnki.1006-3331.2026.03.004
A Study on Multi-parameter Identification of Permanent Magnet Synchronous Motor Based on Multi-strategy Enhanced GWO
LOU Zhijun,CAI Jingcheng,REN Tianliang,ZHANG Jiayu,XUE Wenjia
Abstract: To address the problems of multivariate coupling,strong nonlinearity,and insufficient accuracy of traditional methods in Permanent Magnet Synchronous Motor parameter identification,this paper proposes a Multi-Strategy Improved Grey Wolf Optimizer.The algorithm adopts a hybrid population initialization strategy to enhance the diversity of initial solutions.It employs a dynamic leader-wolf mechanism to strengthen global search and introduces a speed-direction-aware strategy to dynamically optimize the wolf pack's movement trajectory.It incorporates a dual guidance strategy that integrates individual historical optima and the global historical optimum to enhance global optimization ability.The simulation results show that compared with the traditional Grey Wolf Optimizer,the proposed strategy can significantly improve the parameter identification accuracy of the permanent magnet synchronous motor.
Key words: Permanent Magnet Synchronous Motor; online parameter identification; motor control; Grey Wolf Optimizer
