Application of Improved Chicken Flock Algorithm in the Control of Three-Phase Permanent Magnet Synchronous Motor

Abstract

As one of the most important power equipment in industrial society, permanent magnet synchronous motor has a large number of uses in many fields. The traditional three-phase permanent magnet synchronous motor using vector control technology cannot meet the industrial high precision control requirements. So, the motor is analysed, the permanent magnet motor control model is constructed, and the chicken cluster algorithm is used to optimize the vector control parameters of the permanent magnet synchronous motor. Considering that the traditional cluster model faces the problem of local optimal solution, hypercube sampling and particle swarm model are used to optimize the chicken flock model, and an improved chicken flock motor control model is constructed to achieve efficient control of the motor. In the test of Rastrigin function, the particle swarm model, the traditional flock model and the improved flock model converge after 91, 72 and 20 iterations, respectively, and the best objective function values of 3.91e-4, 9.27e-5 and 1.79e-5 are obtained for the three models. The improved flock model has better convergence efficiency and convergence accuracy. In the test of different motor control methods, the overall fluctuation of the improved flock model is smaller, and the stability is improved by 31.65% compared with the traditional vector control, and 11.35% compared with the traditional flock model. The research content has important reference value for intelligent control of motors.