基于改进遗传算法的永磁同步电机多参数辨识
PDF下载 (18)张 腾,徐坚磊,鞠立涛,王 战,胡燕海.基于改进遗传算法的永磁同步电机多参数辨识[J].宁波大学学报(理工版),2026,39(2):36-41.DOI:10.20098/j.cnki.1001-5132.2025.1011
ZHANG Teng,XU Jianlei,JU Litao,WANG Zhan,HU Yanhai.Multi-parameter identification of permanent magnet synchronous motor based on improved genetic algorithm[J].Journal of Ningbo University(Natural Science & Engineering Edition),2026,39(2):36-41.DOI:10.20098/j.cnki.1001-5132.2025.1011
| Title: | Multi-parameter identification of permanent magnet synchronous motor based on improved genetic algorithm |
| 作者: | 张 腾, 徐坚磊, 鞠立涛, 王 战, 胡燕海 |
| Author(s): | ZHANG Teng, XU Jianlei, JU Litao, WANG Zhan, HU Yanhai |
| 关键词: | 永磁同步电机; 遗传算法; 参数辨识; Logistic混沌映射; 信息熵 |
| Keywords: | permanent magnet synchronous motor; genetic algorithm; parameteridentification; logistic chaoticmap; information entropy |
| 分类号: | TM341 |
| DOI: | 10.20098/j.cnki.1001-5132.2025.1011 |
| 文献标识码: | A |
| 摘要: | 针对永磁同步电机参数辨识的传统方法存在难以同时辨识多参数、辨识结果精度低、收敛慢等问题，结合遗传算法特性与电机数学模型，提出了一种改进遗传算法（IGA）。首先采用Logistic混沌映射来初始化种群，提高种群的多样性和均匀性，避免种群初期陷入局部收敛；其次，在遗传算法选择机制中，引入信息熵驱动的自适应选择，避免算法早熟，同时加快收敛速度。这些改进增强了算法的全局和局部搜索能力，提高了算法的鲁棒性。通过三种典型的优化算法测试函数以及仿真实验，将IGA与传统遗传算法（GA）和粒子群优化（PSO）算法等进行对比，实验结果表明：IGA具有更好的鲁棒性、收敛速度以及辨识精度。 |
| Abstract: | In order to solve the problems of difficulty in identifying multiple parameters at the same time, low accuracy and slow convergence of the traditional method of permanent magnet synchronous motor parameter identification, an improved genetic algorithm (IGA) was proposed by combining the characteristics of genetic algorithm and the mathematical model of the motor. Firstly, the Logistic chaotic map was used to initialize the population, improve the diversity and uniformity of the population, and avoid the initial local convergence of the population. Secondly, for the selection mechanism of genetic algorithm, an adaptive selection approach driven by information entropy is introduced to avoid precocious maturity of the algorithm and also to accelerate the convergence speed. Through these improvements, both global and local search capabilities of the algorithm are enhanced, and the robustness of the algorithm is improved. Finally, the proposed IGA is compared with traditional Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm and others by the test functions of three typical optimization algorithms and simulation experiments, with the results showing that the IGA has better robustness, convergence speed and recognition accuracy. |
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| 备注/Memo: | 收稿日期：2025-10-26 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ 基金项目：国家自然科学基金（51705263）；“科创甬江2035”重点研发计划（2025Z042） 第一作者：张 腾，硕士研究生，主要研究方向为智能制造。E-mail: 2463664626@qq.com *通信作者：胡燕海，博士/教授，主要研究方向为智能制造、光机电一体化。E-mail: huyanhai@nbu.edu.cn 宁波大学学报（理工版）网址：http://journallg.nbu.edu.cn/ |
