PID Speed Control of PMSM Based on GA-PSO Algorithm

Due to the nonlinear coupling and external disturbances of permanent magnet synchronous motors, traditional PID control is difficult to balance dynamic response and antidisturbance stability. Therefore, this paper proposes a genetic particle swarm algorithm to optimize the PID speed control of the motor. This algorithm integrates the global search ability of the genetic algorithm and the fast convergence characteristic of the particle swarm algorithm, and performs global optimization on the Kp, Ki, and Kd parameters of the PID controller. With the time multiplied by the absolute error integral (ITEA) as the objective function, through model simulation in MATLAB/Simulink, the fitness curves of genetic particle swarm PID and particle swarm PID, as well as the motor speed waveforms of both, are compared. The results show that the genetic particle swarm PID converges faster, has an extremely fast response speed, a smoother convergence trajectory, and higher computational efficiency. It provides an effective solution for the high-performance control of permanent magnet synchronous motors.