Design of Intelligent Twin-Screw Extruder Control System Based on Improved PSO-BP Neural Network

In the biaxially oriented polyester film production line, the extrusion system is a very important link, and the stability of the extruder pressure directly affects the quality of the film product. In order to solve the problems of hysteresis, high overshoot and low anti-interference ability in the extruder pressure control system of the polyester film production line and make the output of the extruder pressure control system stable at the target value, a new controller is proposed. Traditional Proportional-Integral-Derivative (PID) control is a linear control, and modern control mostly adopts PID control. Therefore, based on particle swarm optimization (PSO), Back Propagation (BP) neural network and PID controller, an improved PSO-BP neural network PID controller is proposed for extruder pressure control system. The new system design combines the actual operation parameters of the twin-screw extruder and other information, and simulates the system based on MATLAB/Simulink module, and compares the control effect with the traditional PID and BP neural network PID controller. The results show that: based on IPSO-BP neural network PID control algorithm, the extruder control system makes the pressure output stable at the target value; the control system has small overshoot, short rise time, strong anti-interference ability, which can improve the quality and intelligence level of the film production line to a certain extent.