[1]张秀玲,陈丽杰,逄宗朋,等.RBF神经网络的板形预测控制[J].智能系统学报,2010,5(01):70-73.
 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(01):70-73.
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第5卷
期数:
2010年01期
页码:
70-73
栏目:
出版日期:
2010-02-25

文章信息/Info

Title:
A predictive system for process control of flatness in rolling mills using a radial basis function network
文章编号:
1673-4785(2010)01-0070-04
作者:
张秀玲12陈丽杰12逄宗朋12朱春颖12贾春玉12
1.燕山大学 电气工程学院,河北 秦皇岛 066004;
2.燕山大学 工业计算机控制工程河北省重点实验室,河北 秦皇岛 066004
Author(s):
ZHANG Xiu-ling12 CHEN Li-jie12 PANG Zong-peng12 ZHU Chun-ying12 JIA Chun-yu12
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
关键词:
板形控制HC轧机液压弯辊控制RBF神经网络预测控制
Keywords:
shape control HCmill hydraulic control of bending rollers RBF neural network predictive control
分类号:
TP18
文献标志码:
A
摘要:
由于板带轧制的环境十分复杂,如温度的变化是无法避免的干扰,以及HC轧机液压弯辊系统的非线性和不确定性,使得按传统理论建立的模型和控制方法都难以达到理想的效果.针对这一问题,提出了一种基于径向基函数(RBF)神经网络的模型预测控制方案应用于带材控制中,以提高带材的成材率,充分发挥液压弯辊力对板形的调整作用,改善轧机系统的动态特性.仿真结果表明了该控制系统的性能良好,有较强的抗干扰能力和较好的鲁棒性和快速性.
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.

参考文献/References:

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相似文献/References:

[1]张秀玲,逄宗鹏,李少清,等.ANFIS的板形控制动态影响矩阵方法[J].智能系统学报,2010,5(04):360.
 ZHANG Xiu-ling,PANG Zong-peng,LI Shao-qing,et al.A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems[J].CAAI Transactions on Intelligent Systems,2010,5(01):360.

备注/Memo

备注/Memo:
收稿日期:2008-10-06.
基金项目:国家自然科学基金资助项目(50675186).
通信作者:张秀玲.E-mail:zxlysu@yahoo.com.cn.
作者简介:
张秀玲, 女,1968年生,教授、博士,主要研究方向为神经网络智能控制.获国家科技进步二等奖1项,省部级科技进步一等奖、二等奖各1项,发表学术论文60余篇.
陈丽杰,女,1982年生,硕士研究生,主要研究方向为神经网络优化板形控制系统设计. 
逄宗朋,男,1983年生,硕士研究生,主要研究方向为模糊神经网络优化板形设计.
更新日期/Last Update: 2010-04-06