Parameter Identification of Permanent Magnet Synchronous Motor Based on Improved Crow Algorithm

To address the issue of parameter identification for permanent magnet synchronous motors (PMSM) using bionic algorithms, which often fall into local optima and result in low identification accuracy, a Crow Particle Optimization Algorithm (CPOA) is proposed. Firstly, the Tent chaotic map is used to optimize the particle swarm algorithm, increasing the diversity of the particle population and enhancing the search accuracy of the algorithm. Secondly, the crow search algorithm is combined with the particle swarm algorithm, utilizing the global search capability of the particle swarm algorithm to guide the local search of the crow algorithm. Finally, the parameters obtained by the improved algorithm are evaluated using a fitness function. Based on the evaluation results, CPOA adjusts the parameters to be identified until the optimal solution is obtained. Experimental results demonstrate that CPOA exhibits superior convergence accuracy and speed in PMSM parameter identification.