Optimisation of PID control for tobacco feeding rail based on convolutional neural network

The tobacco wire guide system is a key component in cigarette production equipment.This paper proposes a tobacco wire guide PID control optimisation model based on convolutional neural network (CNN-PID).The tobacco wire guide speed, guide position, guide temperature, ambient temperature, ambient humidity, and PID parameters at the previous moment are selected as model dependent variables.After normalisation, they are input into the lightweight convolutional neural network.After model parameter adjustment, the predicted proportional gain, integral gain and derivative gain are finally output.After training, the R 2 -score values of CNN-PID on proportional gain, integral gain and derivative gain are 0.992, 0.984, and 0.982, respectively.In addition, the R 2 -score values of the CNN-PID model are better than those of traditional PID control, BP neural network PID, and PSO optimised PID.