[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):360-365.
 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(04):360-365.
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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04期
页码:
360-365
栏目:
出版日期:
2010-08-25

文章信息/Info

Title:
A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems
文章编号:
1673-4785(2010)04-0360-06
作者:
张秀玲逄宗鹏李少清张少宇
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 PANG Zong-peng LI Shao-qing ZHANG Shao-yu 
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:
板形控制自适应神经模糊推理系统影响矩阵聚类模糊 
Keywords:
flatness control adaptive neurofuzzy inference system influence matrix clustering fuzzy
分类号:
TP18
文献标志码:
A
摘要:
针对板形控制系统的非线性和强耦合性,以及传统效应函数法和板形静态影响矩阵法的不足,通过对大量生产实测数据的计算和分析,提出了板形控制的动态影响矩阵法.通过基于减法聚类的ANFIS(自适应神经模糊推理系统)的板形动态矩阵预测模型,在线求得不断变化的影响矩阵,兼顾了板带生产的实时性与复杂性,仿真实验验证了其有效性. 
Abstract:
Flatness control systems have both strong nonlinearity and coupling. Unfortunately traditional effective function methods and the static influence matrix of flatness can not effectively solve such problems. After analysis of a large volume of production data a new method was proposed, a dynamical influence matrix method for the flatness controller. Using the predictive model of the dynamic flatness matrix, and incorporating the subtractive clustering of an adaptive neurofuzzy inference system (ANFIS), the influence matrix was calculated in real time. Both the need for realtime results and the complexities of strip steel production were accommodated. Simulations confirmed the validity of the proposed method. 

参考文献/References:

[1] 张秀玲.冷带轧机板形智能识别与智能控制研究[D]. 秦皇岛:燕山大学,2002.
 ZHANG Xiuling. Research on intelligent control and recognition of flatness for cold strip mill[D].Qinhuangdao: Yanshan University, 2002.
[2]何海涛.宽带钢冷轧机板形在线控制智能模型的研究与应用[D]. 秦皇岛:燕山大学,2008.
HE Haitao. Research on flatness online intelligent control for the wide strip steel cold mill[D]. Qinhuangdao: Yanshan University , 2008.
[3]刘建昌,陈莹莹,张瑞友.基于PSO.BP网络的板形智能控制器[J]. 控制理论与应用,2007,24(4):674-678.
LIU Jianchang, CHEN Yingying, ZHANG Ruiyou. Intelligent flatnesscontroller based on PSOBP network[J]. Journal of Control Theory and Application, 2007, 24(4): 674-678.
 [4]RINGWOOD J V. Shape control systems for Sendzimir steel mills[J].IEEE Transactions on Control Systems Technology, 2000, 8(1): 70-86.
 [5]ZARATE L E, BITTENCOUT F R. Neural networks and fuzzy rules based control for cold rolling process via sensitivity factors[C]//27th Annual Conference of the IEEE Indus trial Electronics Society. Piscataway, USA, 2001: 64-69.
[6]LIU Hongmin, ZHANG Xiuling, WANG Yingrui. Transfer matrix method of flatness control for strip mills[J]. Journal of Materials Processing Technology, 2005, 166(2): 237-242.
[7]贾春玉,单修迎,牛召平.自调整动态神经网络模型及其在带材板形预测中的应用[J]. 钢铁研究学报,2006, 18(12): 50-53.
JIA Chunyu, SHAN Xiuying, NIU Zhaoping. Selfadjusting dynamic neural network model and its application in strip shape prediction[J]. Journal of Iron and Steel Research, 2006, 18(12): 50-53.
[8]贾春玉,单修迎,刘宏民,邱格君.模糊神经板形控制模型在冷轧带钢生产中的应用[J]. 冶金设备,2008,1:1-5
JIA Chunyu, SHAN Xiuying, LIU Hongmin, QIU Gejun. Application of fuzzy nerve flatness control model in cold rolling[J]. Metallurgical Equipment, 2008, 1: 1-5.
[9]吴晓莉,林哲辉. MATLAB辅助模糊系统设计[M]. 西安:西安电子科技大学出版社,2002:62-93.
[10]YING L C, PAN M C. Using adaptive network based fuzzy inference system to forecast regional electricity loads[J]. Energy Conversion and Management, 2008, 49: 205-211
[11]张阿卜.基于减法聚类和自适应神经模糊推理系统的递阶模糊系统的设计[J]. 控制理论与应用,2004, 21(3): 415-418.
ZHANG Abu. Design of hierarchical fuzzy system via subtractive clustering and ANFIS[J].Journal of Control Theory and Application, 2004, 21(3): 415-418.
[12]杨新,张陶红,余刚,柴天佑.基于ANFIS的选矿产品成本预测模型[J]. 系统仿真学报,2007, 19(24): 5688-5691.
YANG Xin, ZHANG Taohong, YU Gang, CHAI Tianyou.Mineral processing product cost forecasting model based on ANFIS[J]. Journal of System Simulation, 2007, 19(24): 5688-5691.

相似文献/References:

[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.
 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(04):70.

备注/Memo

备注/Memo:
收稿日期:2009-06-13.
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研究,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.
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: 2010-09-20