关键词: 永磁同步电机, 参数辨识, 改进粒子群算法, 混沌映射, 高斯扰动

Abstract: Aiming at the problem of slow convergence speed and poor identification accuracy caused by the general particle swarm optimization algorithm in parameter estimation of permanent magnet synchronous motor due to their particles falling into local optimization in the late iteration, a novel approach based on chaotic Sine mapping and Gaussian perturbation was introduced to achieve precise parameter recognition for permanent magnet synchronous motors. A nonlinear random decreasing inertia weight was constructed using chaotic Sine mapping, and a Gaussian perturbation strategy was introduced in the " individual cognition" part of the particle swarm. The Sine function was used to construct the learning
factor. The improved algorithm only needed to collect the stator current, voltage and speed signals of the motor to achieve the accurate identification of multiple parameters of the permanent magnet synchronous motor. The comparative simulation results show that the improved particle swarm algorithm based on chaos mapping and Gaussian perturbation has faster convergence speed and higher recognition accuracy, which is of great significance for the improvement of permanent magnet synchronous motor control performance.

Key words: permanent magnet synchronous motor ( PMSM ), parameter identification, improved particle swarm optimization( PSO) algorithm, chaotic mapping, Gaussian perturbation