基于粒子群算法的抄纸过程PID神经元网络优化控制

PID Neural Network Optimizing Control Based on Particle Swarm Optimization in Paper Process

抄纸过程中定量和水分的控制是一个大纯滞后、强耦合和非线性的系统,本文提出使用粒子群算法优化的PID神经元网络来解决这些控制问题。设计的双PID神经元网络闭环控制系统中,网络结构简单,使用增加动量项的误差反向传播算法,提高了学习速度,减少了系统的反应时间,并采用粒子群算法优化网络的初始权值,克服PID神经网络学习过程中由于权值易陷入局部最优值的缺点,提高了系统的控制精度。仿真结果表明:初始权值优化后的PID神经网络控制系统具有更高的控制精度和更快的响应时间,能更好地实现抄纸过程的解耦控制。这为抄纸过程定量水分的自动控制提供了一种新的方法。

The optimal control of basis weight and moisture content in paper process with strong coupling,nonlinear and large time delay is difficult to achieve. To solve the problem,the optimal PID neural network controller by particle swarm optimization was adopted in the control system. Because the network structure was simple and a modified error back propagation algorithm with momentum factor was used,the learning speed was increased and the reaction time of the system became short. Particle swarm optimization was used to optimize the initial weights of PID neural network to avoid local optimization for obtaining better control accuracy. Simulation results show PID neural network optimizated by the network＇s initial weights is of better adaptability,decoupling ability and robustness in the decoupling control of basis weight and moisture content. It is a new method for the control of basis weight and moisture content in paper process.