[1]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(4):360-365.
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A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 4 Page number: 360-365 Column: 学术论文—人工智能基础 Public date: 2010-08-25
Title:
A dynamic influence matrix method for flatness control based on adaptivenetworkbased fuzzy inference systems
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
Keywords:
flatness control;  adaptive neurofuzzy inference system;  influence matrix;  clustering;  fuzzy
CLC:
TP18
DOI:
-
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. 
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.
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