Theory-Intelligent Dynamic Matrix Model of Flatness Control for Cold Rolled Strips  

作　　者：LIU Hong-min SHAN Xiu-ying JIA Chun-yu 

机构地区：[1]National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Yanshan University [2]State Key Laboratory of Metastable Materials Science and Technology,Yanshan University

出　　处：《Journal of Iron and Steel Research International》2013年第8期1-7,共7页

基　　金：Item Sponsored by National High-Tech Research and Development Project of China(2009AA04Z143);Natural Science Foundation of Hebei Province of China(E2006001038);Hebei Provincial Science and Technology Project of China(10212101D)

摘　　要：In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by using theory and in-telligent methods synthetically. The network model for rapidly calculating the theory effective matrix is established by the BP network optimized by the particle swarm algorithm. The network model for rapidly calculating the meas- urement effective matrix is established by the RBF network optimized by the cluster algorithm. The flatness control model can track the practical situation of roiling process by on-line selVlearning. The scheme for flatness control quantity calculation is established by combining the theory control matrix and the measurement control matrix. The simulation result indicates that the establishment of theory-intelligent dynamic matrix model of flatness control with stable control process and high precision supplies a new way and method for studying flatness on-line control model.In order to increase the precision of flatness control, considering the principle and the measured data of rolling process essence, the theory-intelligent dynamic matrix model of flatness control is established by using theory and in-telligent methods synthetically. The network model for rapidly calculating the theory effective matrix is established by the BP network optimized by the particle swarm algorithm. The network model for rapidly calculating the meas- urement effective matrix is established by the RBF network optimized by the cluster algorithm. The flatness control model can track the practical situation of roiling process by on-line selVlearning. The scheme for flatness control quantity calculation is established by combining the theory control matrix and the measurement control matrix. The simulation result indicates that the establishment of theory-intelligent dynamic matrix model of flatness control with stable control process and high precision supplies a new way and method for studying flatness on-line control model.

关 键 词：flatness control dynamic matrix theory model measured data neural network particle swarm CLUSTER 

分 类 号：TG334.9[金属学及工艺—金属压力加工]