Research on Permanent Magnet Synchronous Motor Speed Control System Based on Improved Genetic Algorithm

Abstract

In order to improve the response speed and stability of the BP neural network controlled permanent magnet synchronous motor speed control process, an improved genetic algorithm is proposed to optimize the BP neural network. The algorithm optimizes the initial distribution of chromosomes on the one hand, and dynamically adjusts on the other hand. The cross-mutation probability formula improves the global search ability of the genetic algorithm, and optimizes the BP neural network weight update strategy. The model is built and simulated in the MATLAB/Simulink environment. The simulation results show that the improved genetic algorithm is used. The response speed of the permanent magnet synchronous motor speed control system with optimized parameters is improved, the anti-interference ability and tracking performance are improved, and the convergence speed of the BP neural network is improved.