基于改进PSO神经网络的板形板厚解耦控制研究  

Research on decoupling control for strip flatness and gauge based on improved PSO

作　　者：周建新 黄剑雄 李钊 Jian-xin ZHOU;Jian-xiong HUANG;Zhao LI(College of Electrical Engineering,North China University of Science and Technology,Tangshan 063000,China)

机构地区：[1]华北理工大学电气工程学院,河北唐山063000

出　　处：《机床与液压》2020年第6期144-149,共6页Machine Tool & Hydraulics

基　　金：河北省教育厅重点项目(ZD2015059)。

摘　　要：针对板形板厚综合系统具备强耦合、非线性、大时滞等特性,传统的控制方法无法对其完成精确解耦,导致控制精度较低。提出一种基于免疫机制的改进粒子群算法,同时借助此算法来优化处理PID神经网络(PIDNN),形成新型PIDNN控制器。利用两个PIDNN解耦控制器对板形板厚综合系统进行控制以降低系统耦合影响。通过仿真结果可以看出,在动态性能与静态性能上,此算法较以往PIDNN解耦控制均存在明显优势。可为控制领域中的解耦问题提供一定的参考。The traditional control method could accurately decouple the strip flatness and gauge complex system owning to the characteristics of heavy delay,nonlinear and strong coupling,which could led to lower control precision.Based on immune mechanism,an improved particle swarm optimization algorithm was developed,and the weights of PID neural network(PIDNN)were optimized by using this algorithm to form a new PIDNN controller.Two new PIDNN controllers were adopted to control the complex system of strip flatness and gauge to reduce the coupling effect of system.The simulation results showed that the present algorithm has more obvious advantages than that of the previous PIDNN decoupling control in both dynamic and static aspects.This study could provide some references for the decoupling problem in the control field.

关 键 词：解耦控制 PID神经元网络 免疫遗传学算法 板形 板厚 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]