基于改进SPBO算法的水下推进永磁同步电机参数辨识  

Parameter Identification of Underwater Propulsion PMSM Based on Improved SPBO

作　　者：鲁子辰 戴晓强[1,2] 王莹 曾庆军 刘维亭[1] LU Zi-chen;DAI Xiao-qiang;WANG Ying;ZENG Qing-jun;LIU Wei-ting(School of Automation,Jiangsu University of Science and Technology,Zhenjiang 212100,China;Zhoushan Jiangke Ship and Ocean Engineering Equipment Research and Development Center,Zhoushan 316021,China;School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212100,China)

机构地区：[1]江苏科技大学自动化学院,镇江212100 [2]舟山市江科船舶与海洋装备研发中心,舟山316021 [3]江苏科技大学计算机学院,镇江212100

出　　处：《科学技术与工程》2023年第16期6908-6916,共9页Science Technology and Engineering

基　　金：江苏省科技项目(BE2018103);舟山市科技项目(2022C13034)。

摘　　要：精确的永磁同步电机(permanent magnet synchronous motor,PMSM)模型是集成电机水下推进器恒转矩控制的关键。为了提高水下推进PMSM参数辨识的精度,提出一种融合Tent映射与t分布变异策略的改进学生心理优化算法辨识水下推进PMSM参数。该算法在标准学生心理优化算法的基础上,引入Tent映射策略增加初始种群多样性,引入t分布变异策略平衡算法的前期与后期搜索能力,提高了算法的收敛速度和收敛精度。仿真和实验结果表明:该算法的辨识误差可达1%以内,且具有更快的收敛速度和更好的辨识稳定性。Accurate permanent magnet synchronous motor(PMSM)model is the key to the constant torque control of the integrated motor underwater thruster.In order to improve the identification accuracy of underwater propulsion PMSM parameters,an improved student psychological based optimization algorithm that combines Tent map and t distribution variation strategy was proposed to identify underwater propulsion PMSM parameters.Based on the standard student psychology based algorithm,the Tent map strategy was introduced the to increase the diversity of the initial population,and the t distribution mutation strategy was introduced to balance the early and late search capabilities of the algorithm,which improved the convergence speed and accuracy of the algorithm.The simulation and experimental results show that the identification error of the algorithm can reach less than 1%,and it has faster convergence speed and better identification stability.

关 键 词：集成电机推进器 永磁同步电机(PMSM) 学生心理优化算法 参数辨识 TENT映射 T分布 

分 类 号：TM351[电气工程—电机]