[1]ZHANG Xiu-ling,CHEN Li-jie,PANG Zong-peng,et al.A predictive system for process control of flatness in rolling mills using a radial basis function network[J].CAAI Transactions on Intelligent Systems,2010,5(1):70-73.
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A predictive system for process control of flatness in rolling mills using a radial basis function network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 1 Page number: 70-73 Column: 学术论文—机器学习 Public date: 2010-02-25
Title:
A predictive system for process control of flatness in rolling mills using a radial basis function network
Author(s):
ZHANG Xiu-ling1; 2;  CHEN Li-jie1; 2;  PANG Zong-peng1; 2;  ZHU Chun-ying1; 2;  JIA Chun-yu1; 2
1.College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
2.Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China
Keywords:
shape control;  HCmill;  hydraulic control of bending rollers;  RBF neural network;  predictive control
CLC:
TP18
DOI:
-
Abstract:
When plate and strip rolling is done in very complex environments, such as high crown (HC) rolling mills, there are many factors that make system control difficult. Factors affecting the flatness of steel sheets include temperature changes as well as nonlinearities that lead to uncertainty about results from bending roller forces. A novel predictive control program was proposed, one employing a radial basis function (RBF) neural network. It ensures flatness by controlling the bending forces of rollers. Simulation results confirmed this scheme has good performance and robustness.
References:
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