基于改进花授粉算法的永磁同步电机参数辨识

针对传统花授粉算法辨识永磁同步电机参数迭代后期易陷入局部最优导致收敛速度慢和寻优精度低的缺陷，提出了一种基于t-分布扰动和高斯扰动的改进花授粉算法（tGFPA），以实现永磁同步电机参数的高精度辨识。首先利用混沌Logistic映射对花朵个体位置进行初始化，然后在全局授粉过程中引入t-分布扰动，提高搜索空间的多样性。在局部授粉过程中加入高斯扰动，增强跳出局部最优解的能力。最后，对比仿真结果表明：基于双扰动策略的改进花授粉算法收敛速度更快、辨识精度更高，对于永磁同步电机控制性能改善具有重要意义。

Parameters Identification of PMSM Based on Improved Flower Pollination Algorithm

In view of the traditional flower pollination algorithm for parameter identification in permanent magnet synchronous motors, the late iteration often results in easily plunging into a local optimum, leading to slow convergence speed and the defect of low optimization accuracy. An improved flower pollination algorithm based on t-distributed perturbation and Gaussian perturbation (tGFPA) is proposed to realize high-precision identification of permanent magnet synchronous motors. Firstly, the individual position of flowers is initialized by chaotic logistic mapping, and then t-distribution perturbation is introduced in the global pollination process to improve the diversity of search space. Gaussian perturbation is added during local pollination to enhance the ability to jump out of the local optimal solution. Finally, comparing the simulation results show that the flower pollinate algorithm based on double perturbation strategy to improve convergence speed is faster and have higher precision, which has important significance for improving control performance of permanent magnet synchronous motor.