【目的】针对元启发式算法在永磁同步电机(PMSM)参数辨识过程中易陷入局部最优的问题,提出一种基于Tent混沌映射和非线性动态自适应权重的改进蜘蛛猴优化(SMO)算法,实现了PMSM内部参数的准确辨识。【方法】通过在算法初始化阶段引入Tent混沌映射,增加了前期搜寻到最优解的概率。在局部领导者阶段引入动态自适应权重,根据当前迭代次数下种群适应度值来满足下一世代种群对于全局探索和局部寻优的需求。【结果】仿真结果显示,基于Tent混沌映射和非线性动态自适应权重的改进SMO在辨识过程中的收敛速度和辨识精度均有所提升,且误差控制在0.5%左右。【结论】本文提出的改进SMO具有更快的识别速度、更高的识别精度和良好的收敛特性。

[Objective] To address the issue of metaheuristic algorithms being prone to falling into local optima during the parameter identification process of permanent magnet synchronous motor (PMSM), an improved Spider Monkey optimization (SMO) algorithm based on Tent chaotic mapping and nonlinear dynamic adaptive weights is proposed. This algorithm aims to achieve accurate identification of internal parameters of PMSM. [Methods] By introducing Tent chaotic mapping in the initialization phase of the algorithm, the probability of finding the optimal solution in the early stage was increased. In the local leader stage, dynamic adaptive weights were introduced based on the population’s fitness values in the current iteration to meet the next generation population’s needs for global exploration and local optimization. [Results] Simulation results showed that the improved SMO algorithm based on Tent chaotic mapping and nonlinear dynamic adaptive weights had improved convergence speed and identification accuracy during the identification process, with errors controlled within approximately 0.5%. [Conclusion] The proposed improved SMO algorithm exhibits faster identification speed, higher accuracy, and good convergence characteristics.

国家自然科学基金(51477023);辽宁省教育厅项目(LJKMZ20220835)