Adaptive PID controller of permanent magnet linear synchronous motor based on particle swarm neural network

Abstract

To improve the dynamic response performance and robustness of a permanent magnet linear synchronous motor (PMLSM)-based servo system, an adaptive proportional-integral-derivative (PID) controller based on a particle swarm optimization neural network is proposed. First, according to the mechanical dynamics equation of the PMLSM, a mathematical model of the PMLSM was established. Second, an adaptive PID speed controller is designed to realize real-time control of the PMLSM. To improve the dynamic performance and stability of the controller, a particle swarm optimization neural network is used to dynamically tune the parameters. Finally, the effectiveness of the proposed controller was verified on the MATLAB/Simulink simulation platform. Compared to the traditional PID controller, the adaptive PID controller can improve the dynamic performance of the system more effectively.