Fuzzy Barrel Temperature PID Controller Based on Neural Network

Abstract

Addressing to the difficulties in PID parameter tuning, low accuracy in temperature controlling and the dissatisfaction in high exactitude extrusion processing of the present PID controllers, a new kind of PID controller based on RBF neural network is proposed. It can not only obtain a higher accuracy in temperature controlling, but also infinitely approach the nonlinear system with quicker and more stable convergence by fuzzy variable step sizes in the adaptive optimizations. The simulation results show that the proposed PID controller shortens the transient response time obviously with good system stability. It has a better performance in the barrel temperature controlling than other traditional PID controllers.