Research on RBF-PID self-learning control of magnetic levitation linear synchronous motor drive system of motion platform

Aiming at the non-linearity, coupling and uncertainty of external disturbance in the magnetic levitation linear synchronous motor drive system of the moving platform, a RBF neural network PID self-learning controller is proposed. Firstly, the structure and operating mechanism of the magnetic levitation linear synchronous motor are introduced in detail; the mathematical model of the magnetic levitation linear synchronous motor is established, and the expressions of the voltage equation, flux linkage equation, horizontal thrust equation and horizontal motion equation are derived; a design based on The PID self-learning controller of RBF neural network. In the RBF neural network, the gradient method is generally used to adjust the parameters w、b、c. In order to effectively shorten the learning time of the network and reduce the oscillation of the system, the paper is in the RBF neural network. The momentum factor item is introduced in, and the control system is simulated through MATLAB to verify that the RBF-PID self-learning algorithm has better stability and anti-disturbance than the PI controller.