基于PID神经网络的纸浆浓度控制

Pulp Consistency Control Based on PID Neural Network

本文将一种不同于用神经网络调整PID参数的新的融合算法—PID神经网络(PIDNN)应用于纸浆浓度控制。经过对纸浆浓度控制系统的仿真研究表明,PIDNN既具有常规PID控制器结构简单的优点,又具有神经网络自学习、自适应之能力,大大改善了纸浆浓度控制系统的性能。

A new type of controller called PIDNN,which coalescences traditional PID and neural network together,is applied to the control of the pulp consistency in this paper.Through the simulation of pulp consistency system,the result shows that the PIDNN has advantages of both neural network and conventional PID.It has good adaptability and strong robustness,with which the performance of pulp consistency control system was greatly improved.