改进蚁狮优化算法的永磁同步电机多参数辨识

Multi-parameter Identification of Permanent Magnet Synchronous Motor Based on Improved Ant Lion Optimizer

为提高永磁同步电机参数辨识的精度,提出一种基于Tent映射和正态云模型的改进蚁狮优化算法辨识永磁同步电机参数。首先,在蚂蚁和蚁狮初始化阶段引入Tent映射使初始种群更加均匀地分布于搜素空间;其次,在蚂蚁位置更新阶段以全局最优个体为目标,使用正态云模型更新精英蚁狮的位置;最后,根据迭代次数的增长自适应缩小云滴的生成范围,提升算法的全局收敛能力和精度。仿真实验结果表明,改进蚁狮优化算法能更加精确、迅速地辨识永磁同步电机参数。

To improve the parameter identification accuracy of a permanent magnet synchronous motor(PMSM),an im⁃proved cloud ant lion optimizer(CALO)based on Tent mapping and normal cloud model is proposed to identify the PMSM parameters.First,Tent mapping is introduced in the initialization phase of ant and ant lion to make the initial population more evenly distributed in the search element space.Second,in the ant position update phase,the global op⁃timal individual is taken as the goal,and the normal cloud model is used to update the position of the elite ant lion.Fi⁃nally,the generation range of cloud droplets is adaptively reduced according to the increase in iteration times,so as to improve the global convergence capability and accuracy of the algorithm.Simulation results show that the improved ant lion optimizer can identify the PMSM parameters more accurately and quickly.