Parameter Identification of Permanent Magnet Synchronous Motor Based on Educational Competition Optimization Algorithm

To address the issue of the heuristic algorithm's low identification accuracy and easy fall into the local optimal when used for parameter identification of permanent magnet synchronous motors, an improved Educational Competition Optimization (ECO) is proposed. First, the particle swarm algorithm is optimized using the logistic chaotic map, increasing the particle swarm's diversity and improving the algorithm's search accuracy. The second step involved using the educational competition optimizer to improve the local exploration ability, and the final step involved using the fitness function to evaluate the parameters obtained by the improved algorithm. The simulation results indicate that the ECO algorithm has high convergence accuracy and speed in the parameter recognition of permanent magnet synchronous motors, and it makes adjustments to the parameters to be identified based on the evaluation results.