基于改进粒子群算法的永磁同步电机参数识别

his paper introduces a particle swarm optimization (PSO) algorithm which fitting function has been improved that uses experimental measurements for the identification of transfer function of permanent magnet synchronous motor (PMSM) in the field of engineering applications. Firstly, the transfer function model including a current controller and space vector modulation is presented. Secondly, the specific weight function is constructed according to the first fitting results involving amplitude, frequency and initial phase obtained by PSO after a series of multi sinusoidal command signals are injected into the PMSM and then frequency characteristics of transfer function model in different frequencies are gained. Subsequently, the transfer function parameters were obtained using the method of Levy. Eventually, the experimental results obtained by improved PSO are compared with other one gained through PSO to demonstrate the accuracy, effectiveness and robustness of the presented method. A 32-bits TMS320F2812 real-time microcontroller and a commercial PMSM has been used to carry out the experimental measurements.