整辊镶块式板形仪信号处理及板形闭环控制方法研究
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摘要
本文以整辊镶块式板形辊的信号处理和板形闭环控制方法为主要内容，展开了理论、工业验证及仿真研究，所取得的成果对我国掌握具有自主知识产权的板形检测与控制技术具有积极的理论和现实意义。
以整辊镶块式板形辊为对象，在分析其信号产生及提取原理的基础上，以提高板形信号检测精度及系统稳定性为目标，对板形信号处理的有关内容展开全面研究，从激磁信号产生方法、板形信号处理系统架构、信号补偿模型及抗干扰方法等方面对板形信号处理理论及方法进行了补充和完善。基于单片机产生激磁信号方法，采用D/A转换方式产生的激磁信号具有稳频、稳幅、稳相特性，可有效避免板形信号在处理过程中的精度损失。在分析传统系统架构模型的不足及其成因的基础上，提出“信号处理板卡+上位机软件”板形信号处理系统架构模型，将信号提取、数据采集与处理等实时性要求很强的功能下移至信号处理板卡并由DSP芯片控制；该模型抗干扰能力强，上位机工作负载小，可有效增强整个系统的实时性、稳定性及可靠性。利用自主研制的变包角实验装置，通过物理实验方法，寻求径向压力与带钢包角、旋转频率之间的关系，建立带钢包角补偿和幅频特性补偿模型；通过理论分析方法，建立适应带钢跑偏情况的带钢边部覆盖补偿模型。同时，研究板形信号处理过程中不同阶段的抗干扰方法，并用于指导板形信号处理系统的研制，从而为提高板形信号处理系统的抗干扰能力及稳定性奠定基础。
以板形信号处理理论及方法为指导，自主研制整辊镶块式板形仪信号处理系统。经标定及通讯测试后，将整辊镶块式板形仪与AFC系统共同投入到鞍钢1250HC冷轧机的工业应用中，并从抗干扰能力、板形检测精度、板形闭环控制效果等方面对板形信号处理系统的性能进行分析与验证。工业应用表明，该系统具有抗干扰能力及实时性强，信号提取及转换精度高，稳定可靠等优点。
针对现有人工神经网络方法求解板形控制影响矩阵的不足，提出基于影响矩阵自学习的板形闭环控制方法。该方法利用事先确定的关键影响因素，建立影响矩阵先验值表；根据实际工况点在影响矩阵先验值表中的位置，利用质心插值原理在线计算影响矩阵，实现板形调控机构调节向量的计算；在各种板形影响因素共同作用下，影响矩阵自学习模型利用轧制过程数据不断修正典型工况点的影响矩阵先验值，使其与板形调控机构的实际调控性能不断接近，进而为板形闭环控制快速地进入稳定轧制期、达到精度要求奠定基础。
采用质心插值原理实现影响矩阵的在线求解，通过影响权重因子及自学习速度因子的动态调节实现影响矩阵的自学习过程；与人工神经网络方法相比，该方法具有计算速度快、稳定性好、可靠性高、适合在线应用、便于实施等特点。利用1250HC冷轧机的有关实测数据，对基于影响矩阵自学习的板形闭环控制方法进行了仿真研究。结果表明，该方法板形控制精度较高，控制过程平稳，收敛速度较快。
Focusing on the signal processing on shape meter of entire roller embedded withelatic bolocks and shape closed-loop control method, theoretical research, industrialapplication and simulation research were carried out in this paper, and the newachievements gained has positive theoretical and practical significance to master the shapemeasuring and controlling technology with proprietary intellectual property rights.
Taking the shape measuring roller embedded with elatic bolocks as research objects，on the basis of analysis on shape signal generation and extraction principle, and aiming toimprove the measuring precision of shape signal and system stability, a body of theory andmethod which is used for the research and development of shape siganl processing system,is supplemented and completed by the related research on shape signal processing fromthe standpoints of excited signal generating method，system architecture of shape signalprocessing, signal compensation model and anti-disturbance methods. This theory mainlyincludes the following content. A method generating high quality excited signal based onsinglechip, which generates the excited signal with the characters of frequency, amplitudeand phase position stabilization by D/A convertion, and this can effectively avoid lossingthe shape signal precision. According to disadvantage and its reason of traditional singleprocessing system architecture model, the system architecture model “signal processingbaord+upper machine software” is proposed. In this model, the functions with highrequirement of real-time, such as signal extraction, data acquisition and Data Processing,are lowered into the signal processing board and controlled by DSP chip. This model hasstrong anti-disturbance ability, lesser work load on the upper machine, and is efficient toenhance the real-time, stability and reliability of this system. The relations among theradial pressure, wrap angle and rotational frequency are gained by physical experimentwith the self-made alterable wrap angle experimental installation, further, thecompensation models for strip wrap angle and amplitude-frequency characteristic arecreated. The compensation model for marginal measuring unit coated by clod strip,suitable for the situation of strip wandering, is created by theoretical analysis.Simultaneously, the anti-distance methods of the different stage in the shape signal processing procedure are researched, and are applied into the development of shape signalprocessing system in order to improve the system anti-distance ability and stability.
The signal processing system of shape meter of entire roller embedded with elaticbolocks is developed under the direction of the above theory. After calibrating anddocking testing in laboratory, the shape meter of entire roller embedded with elaticbolocks and the AFC system are applied together into the industrial application, and fromthe angle of anti-disturbance capacity, shape measuring precision and effect of shapeclosed-loop control, the performances of signal processing system are analyzed andverified. The industrial application presented that the shape signal processing system hasstrong anti-disturbance ability, powerful real-time performance, high precision ofextraction and convertion, more stability and reliability.
Directing at the defects of artificial neural networks method to obtain shape controleffective matrix, a shape closed-loop control method based on self-learning of effectivematrix is proposed. The effective matrix empirical value table is created by keyinfluencing factors determined beforehand. According to the position of actual workingcondition point in the effective matrix empirical value table, the value of effective matrixis calculated on-line by centroid interpolation principle, further, the regulating variables ofshape control actuator are gained. Under the combined action of the various shapeinfluencing factors, self-learning model of effective matrix makes use of the rollingprocess data to correct the effective matrix empirical value of the relevant typical workingcondition points. This makes the effective matrix empirical values approximate to theactual regulation performances, and provides the foundation for the shape closed-loopcontrol which enters the stability rolling period and reachs to the required precision asquickly as possible.
The on-line calculating of effective matrix is realized by centroid interpolationprinciple, and the realization of self-learning process of effective matrix is made by weightfactors and dynamic regulating of self-learning speed factor. Compared with the artificialneural networks method, this method has rapid computation speed, good stability andreliability, and is suitable to on-line application and easy to implement. Using the relevantreal data of1250HC cold mill, the simulation of the shape closed-loop control method based on self-learning of effective matrix is carried out. The simulation result shows thismethod has better shape control precision, smooth control process and higher convergingspeed.
以整辊镶块式板形辊为对象，在分析其信号产生及提取原理的基础上，以提高板形信号检测精度及系统稳定性为目标，对板形信号处理的有关内容展开全面研究，从激磁信号产生方法、板形信号处理系统架构、信号补偿模型及抗干扰方法等方面对板形信号处理理论及方法进行了补充和完善。基于单片机产生激磁信号方法，采用D/A转换方式产生的激磁信号具有稳频、稳幅、稳相特性，可有效避免板形信号在处理过程中的精度损失。在分析传统系统架构模型的不足及其成因的基础上，提出“信号处理板卡+上位机软件”板形信号处理系统架构模型，将信号提取、数据采集与处理等实时性要求很强的功能下移至信号处理板卡并由DSP芯片控制；该模型抗干扰能力强，上位机工作负载小，可有效增强整个系统的实时性、稳定性及可靠性。利用自主研制的变包角实验装置，通过物理实验方法，寻求径向压力与带钢包角、旋转频率之间的关系，建立带钢包角补偿和幅频特性补偿模型；通过理论分析方法，建立适应带钢跑偏情况的带钢边部覆盖补偿模型。同时，研究板形信号处理过程中不同阶段的抗干扰方法，并用于指导板形信号处理系统的研制，从而为提高板形信号处理系统的抗干扰能力及稳定性奠定基础。
以板形信号处理理论及方法为指导，自主研制整辊镶块式板形仪信号处理系统。经标定及通讯测试后，将整辊镶块式板形仪与AFC系统共同投入到鞍钢1250HC冷轧机的工业应用中，并从抗干扰能力、板形检测精度、板形闭环控制效果等方面对板形信号处理系统的性能进行分析与验证。工业应用表明，该系统具有抗干扰能力及实时性强，信号提取及转换精度高，稳定可靠等优点。
针对现有人工神经网络方法求解板形控制影响矩阵的不足，提出基于影响矩阵自学习的板形闭环控制方法。该方法利用事先确定的关键影响因素，建立影响矩阵先验值表；根据实际工况点在影响矩阵先验值表中的位置，利用质心插值原理在线计算影响矩阵，实现板形调控机构调节向量的计算；在各种板形影响因素共同作用下，影响矩阵自学习模型利用轧制过程数据不断修正典型工况点的影响矩阵先验值，使其与板形调控机构的实际调控性能不断接近，进而为板形闭环控制快速地进入稳定轧制期、达到精度要求奠定基础。
采用质心插值原理实现影响矩阵的在线求解，通过影响权重因子及自学习速度因子的动态调节实现影响矩阵的自学习过程；与人工神经网络方法相比，该方法具有计算速度快、稳定性好、可靠性高、适合在线应用、便于实施等特点。利用1250HC冷轧机的有关实测数据，对基于影响矩阵自学习的板形闭环控制方法进行了仿真研究。结果表明，该方法板形控制精度较高，控制过程平稳，收敛速度较快。
Focusing on the signal processing on shape meter of entire roller embedded withelatic bolocks and shape closed-loop control method, theoretical research, industrialapplication and simulation research were carried out in this paper, and the newachievements gained has positive theoretical and practical significance to master the shapemeasuring and controlling technology with proprietary intellectual property rights.
Taking the shape measuring roller embedded with elatic bolocks as research objects，on the basis of analysis on shape signal generation and extraction principle, and aiming toimprove the measuring precision of shape signal and system stability, a body of theory andmethod which is used for the research and development of shape siganl processing system,is supplemented and completed by the related research on shape signal processing fromthe standpoints of excited signal generating method，system architecture of shape signalprocessing, signal compensation model and anti-disturbance methods. This theory mainlyincludes the following content. A method generating high quality excited signal based onsinglechip, which generates the excited signal with the characters of frequency, amplitudeand phase position stabilization by D/A convertion, and this can effectively avoid lossingthe shape signal precision. According to disadvantage and its reason of traditional singleprocessing system architecture model, the system architecture model “signal processingbaord+upper machine software” is proposed. In this model, the functions with highrequirement of real-time, such as signal extraction, data acquisition and Data Processing,are lowered into the signal processing board and controlled by DSP chip. This model hasstrong anti-disturbance ability, lesser work load on the upper machine, and is efficient toenhance the real-time, stability and reliability of this system. The relations among theradial pressure, wrap angle and rotational frequency are gained by physical experimentwith the self-made alterable wrap angle experimental installation, further, thecompensation models for strip wrap angle and amplitude-frequency characteristic arecreated. The compensation model for marginal measuring unit coated by clod strip,suitable for the situation of strip wandering, is created by theoretical analysis.Simultaneously, the anti-distance methods of the different stage in the shape signal processing procedure are researched, and are applied into the development of shape signalprocessing system in order to improve the system anti-distance ability and stability.
The signal processing system of shape meter of entire roller embedded with elaticbolocks is developed under the direction of the above theory. After calibrating anddocking testing in laboratory, the shape meter of entire roller embedded with elaticbolocks and the AFC system are applied together into the industrial application, and fromthe angle of anti-disturbance capacity, shape measuring precision and effect of shapeclosed-loop control, the performances of signal processing system are analyzed andverified. The industrial application presented that the shape signal processing system hasstrong anti-disturbance ability, powerful real-time performance, high precision ofextraction and convertion, more stability and reliability.
Directing at the defects of artificial neural networks method to obtain shape controleffective matrix, a shape closed-loop control method based on self-learning of effectivematrix is proposed. The effective matrix empirical value table is created by keyinfluencing factors determined beforehand. According to the position of actual workingcondition point in the effective matrix empirical value table, the value of effective matrixis calculated on-line by centroid interpolation principle, further, the regulating variables ofshape control actuator are gained. Under the combined action of the various shapeinfluencing factors, self-learning model of effective matrix makes use of the rollingprocess data to correct the effective matrix empirical value of the relevant typical workingcondition points. This makes the effective matrix empirical values approximate to theactual regulation performances, and provides the foundation for the shape closed-loopcontrol which enters the stability rolling period and reachs to the required precision asquickly as possible.
The on-line calculating of effective matrix is realized by centroid interpolationprinciple, and the realization of self-learning process of effective matrix is made by weightfactors and dynamic regulating of self-learning speed factor. Compared with the artificialneural networks method, this method has rapid computation speed, good stability andreliability, and is suitable to on-line application and easy to implement. Using the relevantreal data of1250HC cold mill, the simulation of the shape closed-loop control method based on self-learning of effective matrix is carried out. The simulation result shows thismethod has better shape control precision, smooth control process and higher convergingspeed.
引文
[1]国务院办公厅.钢铁产业调整及振兴规划[R/OL].(2009-3-20)[2011-7-15]. http://wenku.baidu.com/view/d909dbea998fcc22bcd10d2e.html.
[2]国家发展改革委员会.国家产业结构调整指导目录[R/OL].(2009-12-20)[2011-7-15].http://wenku.baidu.com/view/0974b4106c175f0e7cd137ff.html.
[3]郑岗,谢云鹏,刘丁,等.板形检测与板形控制方法[J].重型机械,2002,(4):1-3,7.
[4]郑岗,谢云鹏,刘丁,等.板形检测与板形控制方法(续)[J].重型机械,2002,(5):1-3.
[5]丁树峰,胡伟.轧制板带材的板形检测[J].机械设计与制造,2006(9):101-103.
[6]梁勋国,矫志杰,王国栋,等.冷轧板形测量技术概论[J].冶金设备,2006,(6):36-39.
[7]张清东,陈先霖,何安瑞,等.冷轧带钢板形检测与自动控制[J].钢铁,1999,34(10):69-72.
[8] Dere A.shaw.带钢板形控制技术及目标板形的经济影响[J].钢铁技术,2004,(5):40-50.
[9] Anders G.Carlstedt. Modern approach to flatness measurement and control in cold rolling[J].Ironand steel engineer,1991,68(4):34-37.
[10]王快社,王训宏,张兵,等.板形检测控制新方法[J].重型机械,2004,(5):18-22.
[11] Ake Nilsson. Automatic flatness control system for cold rolling mills[J].Iron and steel engineer,1979,56(6):55-60.
[12]胡国栋,王琦,邹本友.磁弹变压器差动输出式冷轧带材板形仪[J].钢铁,1994,29(4):56-59,33.
[13]王国栋.板形控制和板形理论[M].北京:冶金工业出版社,1986:154-162,185-189,190-194,210-220.
[14]杨溪林.激光板形测量技术现状及发展[J].冶金自动化,1998,(1):29-30.
[15]刘江.直线型激光板形检测系统[J].钢铁研究学报,2003,15(5):60-63.
[16]钟春生.新的板形检测方法及其装置[J].钢铁,1995,(1):72-75.
[17] Vladimir B. Ginzburg. Selection of optimum strip profile and flatness technology for rollingmills [J]. Iron and steel engineer,1997(7):30-37.
[18] Rolang Hequet. The two stand reversing cold mill[J].Steel times international,1997,(3):39–40.
[19]刘宏民,于丙强,华仲新,等.分片压磁式板形仪:中国, ZL200610048380.1[P],2008-10-1.
[20]刘浩. ABB板形测量系统在宝钢1800mm冷连轧机组的应用[J].冶金自动化,2006,(1):61-62.
[21] Willems, Stefan. S6-high stand for in-line and tandem application [J]. MPT metallurgical plantand technology international,2010,33(3):34-37.
[22]张华. SUNDWIG板型仪系统在冷轧机中的应用[J].可编程控制器与工厂自动化,2010,(8):58-60.
[23] Reinen A.G., Van der Poel, A.A. Preliminary results of the first tinplate mill vidimon-vidiplanshape control system[J]. Steel times,1983,211(11):575-576.
[24]王启顺.空气轴承式板形仪的常见故障及对策[J].轻合金加工技术,2003,31(7):13-16.
[25] Cook C.W., Ames E.S. Borehole-inclusion stressmeter measurements in bedded stalt[J]. U.S.symposium on rock mechanics,1979:481-489.
[26] Jaworski G.w., Dorwart B.C., White W.F., etc.. Behavior of a rigid inclusion stressmeter in ananisotropic stress field[C]. Proceedings-Symposium on rock mechanics,1982:211-218.
[27] Liu Yu-li, JIN Xiao-guang, Lian Jia-chuang. Elastic plastic3D deformation and stress analysis ofstrip rolling [J]. Journal of iron and steel research international,1998,5(2):29-34.
[28]谢云鹏.板形检测与板形控制研究[D].西安：西安理工大学工学硕士学位论文,2002:18-19.
[29]于丙强.整辊智能型冷轧带钢板形仪研制及工业应用[D].秦皇岛：燕山大学工学工学博士学位论文,2010:16-17,31-32.
[30]刘佳伟,张殿华,王鹏飞,等.板形测量辊输出信号处理方法的研究与应用[J].钢铁研究学报,2010,22(2):59-62.
[31]杨景明,王振臣.板形检测辊输出信号的通道处理方法[J].自动化仪表,1997,18(5):34-37.
[32] Neuschutz E, Nucke G, Thies H. New generation BFI flatness measuring system [J]. Metall. planttechnol. Int.,1994,17(1):86-88.
[33]何海涛.900HC冷轧机板形仪测控系统的研制[J].计算机测量与控制,2003,11(7):496-497,518.
[34]杨铁林.高精度气动板形仪基础技术的研究[D].秦皇岛：燕山大学工学博士学位论文,2001:59-60.
[35]刘健.气动板形仪测控系统实验研究及板形理论建模仿真[D].秦皇岛：燕山大学工学博士学位论文,2008:56-57.
[36]刘宏民.板形理论的研究现状与发展方向[J].钢铁,1993,28(2):75-78.
[37]王文明,钟掘,谭建平.板形控制理论与技术进展[J].矿冶工程,2001,21(4):70-72.
[38]刘宏民.三维轧制理论及其应用—模拟轧制过程的条元法[M].北京:科学出版社,1999:57-68.
[39]彭艳,刘宏民,胡建平,等.冷轧带材板形分析及预设定控制软件(FAPCSR)及其应用[J].钢铁,2003,38(2):34-37.
[40]刘宏民,丁开荣,李兴东,等.板形标准曲线的理论计算方法[J].机械工程学报,2008,44(8):137-142.
[41]王东城.基于机理的带钢连轧机板形在线设定控制模型研究[D].秦皇岛：燕山大学工学博士学位论文,2008:4-14.
[42]林振波,张波,连家创,等.冷轧带材板形判别模型的分析与讨论[J].钢铁,1995,30(8):39-43.
[43] Lian Jiachuang, et al.. Study on the large deflection buckling deformation for rolled strip losingstability[C]. Proc. Int. Conf. on steel rolling. Deauville, France,1987, E5:1-7.
[44] Takashi Ishikawa, et al.. Analytical approach to occurrence and suppression of flatness defect instrip rolling[C]. Proc. Int. Conf. on steel rolling. Deauville, France,1987, E7:1-10.
[45]杨荃.冷轧带钢翘曲形状分析与浪形函数[J].钢铁,1993,28(6):41-45.
[46]刘宏民,彭艳,褚宇鹏.板带轧制板形判别的条元法[J].燕山大学学报,2002,26(2):95-98.
[47] Liu Hongmin,Peng Yan,Chu Yupeng,Wang Yingrui.Strip element method for shape discrimitionof strip rolling[C].Communications in numerical methods in engineering,2004,20(9):709-720.
[48]孙亚波,刘宏民,彭艳.板带轧制板形判别的降阶模型[J].工程力学,2009,26(12):204-210.
[49]华建新,周泽雁.冷轧带钢板形缺陷的多项式回归及数学模型[J].钢铁,1992,27(3):27-31.
[50]刘进.冷轧带钢板形缺陷表达式回归及数学模型[J].轧钢,1996,(5):5-9.
[51] Carlstedt A.G., Keijse O.. Modern approach to flatness measurement and control in cold rolling[J]. Iron and steel engineer,1991,(4):34-39.
[52]邸洪双,张晓峰,刘相华,等.冷轧薄带板形检测信号正交多项式分解及数学模型[J].钢铁,1995,3(9):33-36.
[53]周旭东,王国栋.冷轧板形正交多项式分解模型[J].钢铁,1997,32(8):46-47.
[54] Ikuya Hoshino, Masateru Kawai, Misao Kekubo, et al. Observer-based multivariable flatnesscontrol of the cold rolling mill[C].12th World international federation of automatic control,1993,(6):149-156.
[55]彭艳.基于条元法的HC冷轧机板形预设定控制理论研究及工业应用[D].秦皇岛：燕山大学工学博士学位论文,2000:60-65.
[56]单修迎,刘宏民,贾春玉.含有三次板形的新型板形模式识别方法[J].钢铁,2010,45(8):56-60.
[57]黄文俊. BP神经网络在板形缺陷识别中的应用[D].无锡：江南大学工学硕士学位论文,2008:8-9.
[58]刘宏民,单修迎,贾春玉.智能方法在板形控制中的应用[J].燕山大学学报,2010,34(1):1-5,7
[59] Willis M J,Montague G A,Di Massimo C,et al.. Artificial neural networks in process estimationand control[J].Automatica,1992,28(6):1181-1187.
[60] Peng Yan, Liu Hongmin. A neural network recognition method of shape pattern[J]. Journal ofiron and steel reseach international,2001,8(1):16-20.
[61]刘建,王益群,孙福,等.基于粒子群理论的板形模糊模式识别方法[J].机械工程学报,2008,44(1):173-178.
[62] Jia Chunyu, Shan Xiuying, Liu Hongmin, et al. Fuzzy neural model for flatness patternrecognition[J]. Journal of iron and steel research, International,2008,15(6):33-38.
[63]赵小燕,张朝晖,蓝金辉.基于二叉树型分层BP模型的板形模式识别[J].北京科技大学学报,2009,31(2):261-265.
[64]张秀玲,张志强.DHNN优化设计新方法及在板形模式识别的应用[J].智能系统学报,2008,3(3):250-253.
[65]张秀玲,陈丽杰,季颖,等.基于径向函数神经网络的板形模式识别研究[J].工业仪表与自动化装置,2009,(3):7-9.
[66]张秀玲,逄宗鹏,李少清,等.基于自适应神经模糊推理系统的板形模式识别[J].钢铁研究学报,2009,21(9):59-62.
[67]张秀玲,李少青,田力勇. Elman神经网络的板形模式识别方法[J].智能系统学报,2010,5(5):449-453.
[68] Yu Bingqiang, Sun Yabo, Liu Hongmin, etc.. Compensation model for shape measuring of coldstrip rolling [J]. Journal of iron and steel research, International,2010,17(6):21-26.
[69] Rubén Usamentiaga, Daniel F. García. Compensation for uneven temperature in flatness controlsystems for steel strips[C]. Conference record of the2006IEEE,2006,1:521-528.
[70] Peng Yan, Liu Hong-min. Coupling solved roll contact pressure transverse distribution of HCstrip mill[J].Trans.Nonferrous Met.Soc.Chian,2001,11(6):891-894.
[71] Jia-wei Liu, Dian-hua Zhang, Jun-sheng Wang. Deflection compensation model for flatnessmeasuring roll[J].Journal of iron and steel research, International,2010,17(12):35-37.
[72]杨荃,陈先霖.冷轧机的板形控制目标模型[J].北京科技大学学报,1995,17(3):254-258.
[73]林振波,段振勇,连家创,等.冷轧机板形标准曲线的分析及选择方法[J].钢铁研究学报,1997,19(3):55-58.
[74]贾春玉,尚志东.冷轧板形目标曲线的补偿设定[J].钢铁研究学报,2000,12(4):64-67.
[75]白剑,张清东,常铁柱,等.1420冷轧机组第1机架板形控制目标曲线研究[J].钢铁,2007,42(10):56-59.
[76]王鹏飞,张殿华,刘佳伟,等.冷轧板形目标曲线设定模型的研究与应用[J].钢铁,2010,45(4):50-55.
[77] T.Furuya, et al. Neww design6-high cold mill (HC mill) solves shape problem[G]. AISE yearbook,1979:187-282.
[78]张清东.宽带钢冷轧机板形自动控制系统的研究[D].北京:北京科技大学工学博士学位论文,1994:15-25.
[79]张云鹏.宽带钢冷轧机板形控制效应函数研究[D].北京:北京科技大学工学博士学位论文,1997:10-13.
[80]彭艳. HC轧机板形控制模型研究[D].秦皇岛:燕山大学工学硕士学位论文,1997:4-9.
[81]张清东,陈先霖,何安瑞,等.冷轧宽带钢板形检测与自动控制[J].钢铁,1999,34(10):69-72.
[82]丁修堃.轧制过程自动化[M].第2版.北京:冶金工业出版社,2009:268-275,233.
[83] Teruhiro Saito, Takeo Ohnishi, Tomio Komatsu. Automatic flatness control system in tandemcold rolling mill for ultra-thin gauge strip [J]. Kawasaki steel technical,1991,(24):41-46.
[84] Davies, W.E., Sivilotti, O.G., Tulett, M.W. Production and control of strip flatness in coldrolling [J]. Metals technology,1975,(10):494-498.
[85] Roger Davidson. Automatic flatness control system[J].Iron and steel engineer,1986,(10):44-50.
[86]林振波,连家创,刘宏民. ABB板形测量和控制系统及板形标准曲线[J].轧钢,1994,(4):20-24.
[87] Cfaire NaPPez, SergeBoulot, Richard C MeDermott. Control of strip flatness in cold rolling: aglobal approach[J].Iron and steel engineer,1997(4):42-45.
[88] Sean J. Egan..Flatness Modelling and control for a continuous tandem cold mill [J]. Iron andsteel engineer,1996,(3):38-41.
[89]王长松,张清东,张云鹏.效应函数在冷轧机板形控制中的应用[J].轧钢,1999,(4):28-30.
[90]张云鹏,张清东,王长松.基于效应函数的冷轧机板形闭环控制策略[J].北京科技大学学报,1999,21(2):195-197.
[91] Peng Yan,Liu Hongmin, R.Du. A neural network-based shape control system for cold rollingoperations [J]. Journal of materials processing technology,2008,(202):54-60.
[92] H.T Zhu, Z.Y Jiang, A.K Tieu, G.D Wang.A fuzzy algorithm for flatness control in hot strip mill[J]. Journal of materials processing technology,2003,140(1-3):123-128.
[93] Carlos Thadeu de Auila Pire, Henrique Cezar Ferreira, Reberto Moura Sales. Adaptation fortandem cold mill models[J]. Journal of materials processing technology,2009,209:3592-3596.
[94] Luis E. Zárate, Fabrício R. Bittencout. Representation and control of the cold rolling processthrough artificial neural networks via sensitivity factors[J]. Journal of materials processingtechnology,2008,197(1-3):344-362.
[95]张秀玲.冷带轧机板形智能识别与智能控制研究[D].秦皇岛:燕山大学工学博士学位论文,2002:87-99.
[96]张秀玲,刘宏民.板形控制的传递矩阵方法[J].机械工程学报,2003,39(11):100-103.
[97] Liu Hongmin, Zhang Xiuling, Wang Yingrui. Transfer matrix method of flatness control for stripmills[J]. Journal of materials processing technology,2005,166(2):237-242.
[98] Liu Hong-Min, Zhang Xiu-Ling, Yang Li-Po. Matrix equation and its applications of flatnesscontrol for strip mills[C]. AISTech2006-Iron and steel technology conference proceedings,2006,2:97-108.
[99]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:燕山大学工学博士学位论文,2008:41-75.
[100] Liu Hongmin, He Haitao, Shan Xiuying, ect. Flatness control based on dynamic effective matrixfor cold strip mills [J]. Chinese journal of mechanical engineering,2009,22(2):1-10.
[101]单修迎.基于机理-智能的带钢冷轧机高精度板形控制研究[D].秦皇岛:燕山大学工学博士学位论文,2010:11-33,36-63.
[102]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,2008,5(4):360-365.
[103] LI ZM, Kong LF.New modeling method of soft sensor based on support vector set-RBF neuralnetwork[C]. International conference on informational technology and environmental systemScience,2008,2:1151-1156.
[104]张清东,李博,郑武,等.冷轧机板形闭环反馈控制策略及模型研究.系统仿真学报,2009,21(24):7858-7862.
[105]王崇涛,杨荃,彭鹏,等.宽带钢冷连轧机动态板形控制策略[J].北京科技大学学报,2009,31(4):492-498.
[106] Alberto Bemporad, Daniele Bernardini, Francesco Alessandro Cuzzola, etc. Optimization-basedautomatic flatness control in cold tandem rolling [J]. Journal of process control,2010,20(4):396-407.
[107]曹忠华,朱文豪,李志锋,等.冷轧机板形分段冷却控制系统分析[J].鞍钢技术,2010,(6):31-34.
[108] Mohammad Abbaspour, Ahmad Saboonchi. Work roll thermal expansion control in hot stripmill[J]. Applied mathematical modelling,2008,32(12):2652-2669.
[109] A. Saboonchi, M. Abbaspour. Changing the geometry of water spray on milling roll and its effecton the work roll temperature [C]. Process. Technol,2004,(148):35–49.
[110] Shohet K N, Townsend N A. Roll bending methods of crown control in four-high plate mills[J].Journal of the iron and steel institute,1968,206(11):1088-1098.
[111] Xin-yi Ren, Feng-shan Du, Hua-gui Huang. Application of improved fuzzy immune PIDcontroller to bending control system [J]. Journal of iron and steel research, International,2011,18(3):28-33.
[112] Yin Guo-fang, Wang Yi-qun, Sun Xu-guang. Predictive control of electro-hydraulic bendingcontrol system[J]. Hydraulics pneumatics and seals,2003,(4):21-26.
[113] R-Min Guo. Characteristics of rolling mills with roll shifting[J].Iron and steel engineer,1988,(12):45-52.
[114] J.S. Wang, Z.Y. Jiang, A.K. Tieu, X.H. Liu, etc. Analysis of thrust force in a work roll shiftingmill[J]. International journal of mechanical sciences,2006,48(10):1095-1102.
[115]孙蓟泉,周永红.板形控制技术及应用[J].鞍钢技术,2006,(4):6-11.
[116] Klamma K. CVC technology in cold rolling mills[J]. Metallurgical plant and technology,1985,8(3):60-62;66-67.
[117] Bald W, Beisemann G, Feldmann H, etc. Continuously variable crown(CVC) rolling[J]. Iron andsteel engineer,1987,64(3):32-41.
[118] Nakajima Koei, Kawamoto Toshiharu, Hatae Seiryo. Basic characteristics of pair cross mill[J].Technical review-Mitsubishi heavy industries,1985,22(2):143-148.
[119] Kamada Shunji, Iyama Shoichi, Hamada Ryuji. Edge profile control using pair cross mill in coldrolling [J]. Iron and steel engineer,1996,73(6):20-26.
[120] Hrioshi Aralta,Tsuneo Nakano.Expansion of pair cross(PC) mill applications to hot and coldrolling mills [J].Iron and Engineer,1993,(3):32-37.
[121] Wang B.W., Hu B.J.. Study of rolling force distribution along the rolling of pair cross mill [J].Journal of university of science and eechnology beijing Mineral metallurgy materials (Eng Ed),1996,3(2):107-113.
[122] R G Parker,P J Sathe. Free vibration and stability of aspinning disk-spindle system [J]. Journal ofvibration and acoustics,1999,121:391-396.
[123] J Kipkhope, G J Wilson. Vibration and stress analysis of the rotating discs using annular finiteelements[J].Journal of sound and vibration,1976,44(4):461-474.
[124]肖宏,谢红飙,张国民. PC轧机轧制过程耦合数值模拟研究[J].工程力学,2005,22(3):216-219,215.
[125] Nagai Toshihiko, Yamada Junzo, Masui Takeshi. Strip shape and profile control with sumitomovariable crown roll system [J]. Iron and steel engineer,1983,60(1):56-63.
[126] Masui, T., Nagai, T., Takigawa, T.. Current applications of sumitomo variable crown roll systemto ferrous and non-ferrous metal strip mills [J]. Inst of metals,1985:143-151.
[127] Anon. Shape control in cold rolling by combining variable crown and roll bending [J]. Timesinternational,1997,(3):34-40.
[128]白振华,连家创. VC轧机板形控制关键技术的开发与研究[J].中国机械工程,2003,14(15):1287-1289.
[129] Becker, B., Legrand, N., Vallet, D.,etc.. DSR technology to improve strip flatness on a steeltandem cold mill [J]. Revue de metallurgie. Cahiers D'Informations techniques,2000,97(6):799-813.
[130] Wang, C.W., Perret, G., Goudet, J.,etc.. Installation of the first DSR for steel application atbaosteel: First results [J]. Revue de Metallurgie. Cahiers D'Informations Techniques,1998,95(10):1287-1297.
[131] Wang, J.-F., Jao, L., Faure, J.-P..Operational results of the DSR installed on the No.1tandemcold mill of baoshan iron and steel[J]. Revue de Metallurgie. Cahiers D'Informations Techniques,2005,102(3):241-249.
[132]张清东,王文广,周晓敏. DSR宽带钢冷轧机的特殊板形控制性能[J].北京科技大学学报,2008,30(1):71-76.
[133]单成祥.传感器的理论与设计基础[M].北京:国防工业出版社,1999:311-326.
[134]张国忠,赵家贵.检测技术[M].北京:中国计量出版社,1998:363-378.
[135]安宗权,汪云祥. PCB排版对提高单片机应用系统可靠性的研究[J].安徽电机学院学报,2002,17(3):34-37.
[136]张清东,黄纶伟,周晓敏.宽带钢轧机板形控制技术比较研究[J].北京科技大学学报,2000,22(2):177-181.
[137]徐乐江.板带冷轧机板形控制与机型选择[M].北京:冶金工业出版社,2007:164-177.
[138]李连平,赵志毅,孙彤彤,等.热轧薄带钢板形影响因素的离线模拟分析[J].钢铁,2006,41(7):49-53.
[139]张小平,郭会光,张雪娜,等.板宽对板形的实验研究[J].太原科技大学学报,2009,30(3):225-227.
[140]张雅琴,张小平,郭会光,等.冷轧带钢残余应力及其对板形影响的研究[J].塑性工程学报,2009,16(5):75-79.
[141]张小平,张少琴,何宗霖,等.张力对板形影响的实验研究[J].太原科技大学学报,2009,30(4):312-315.
[142]马超,张国强,赵德华,等.弯辊力对带钢板形的模拟研究[J].鞍钢技术,2009,(5):36-40.
[143] Vladimir B.Ginzburg. High-quality steel rolling: theory and practice[M].New york: MarcelDekker,Inc.,2000:349-352,366.
[144]杨代纶.基于质心的二维和三维插值算法[J].计算机工程与应用,2004,(12):77-78.
[145]杨代纶,勾成俊,唐志全,等.矩阵模型光子束计量算法的改进[J].核技术,2004,27(3):222-227.
[2]国家发展改革委员会.国家产业结构调整指导目录[R/OL].(2009-12-20)[2011-7-15].http://wenku.baidu.com/view/0974b4106c175f0e7cd137ff.html.
[3]郑岗,谢云鹏,刘丁,等.板形检测与板形控制方法[J].重型机械,2002,(4):1-3,7.
[4]郑岗,谢云鹏,刘丁,等.板形检测与板形控制方法(续)[J].重型机械,2002,(5):1-3.
[5]丁树峰,胡伟.轧制板带材的板形检测[J].机械设计与制造,2006(9):101-103.
[6]梁勋国,矫志杰,王国栋,等.冷轧板形测量技术概论[J].冶金设备,2006,(6):36-39.
[7]张清东,陈先霖,何安瑞,等.冷轧带钢板形检测与自动控制[J].钢铁,1999,34(10):69-72.
[8] Dere A.shaw.带钢板形控制技术及目标板形的经济影响[J].钢铁技术,2004,(5):40-50.
[9] Anders G.Carlstedt. Modern approach to flatness measurement and control in cold rolling[J].Ironand steel engineer,1991,68(4):34-37.
[10]王快社,王训宏,张兵,等.板形检测控制新方法[J].重型机械,2004,(5):18-22.
[11] Ake Nilsson. Automatic flatness control system for cold rolling mills[J].Iron and steel engineer,1979,56(6):55-60.
[12]胡国栋,王琦,邹本友.磁弹变压器差动输出式冷轧带材板形仪[J].钢铁,1994,29(4):56-59,33.
[13]王国栋.板形控制和板形理论[M].北京:冶金工业出版社,1986:154-162,185-189,190-194,210-220.
[14]杨溪林.激光板形测量技术现状及发展[J].冶金自动化,1998,(1):29-30.
[15]刘江.直线型激光板形检测系统[J].钢铁研究学报,2003,15(5):60-63.
[16]钟春生.新的板形检测方法及其装置[J].钢铁,1995,(1):72-75.
[17] Vladimir B. Ginzburg. Selection of optimum strip profile and flatness technology for rollingmills [J]. Iron and steel engineer,1997(7):30-37.
[18] Rolang Hequet. The two stand reversing cold mill[J].Steel times international,1997,(3):39–40.
[19]刘宏民,于丙强,华仲新,等.分片压磁式板形仪:中国, ZL200610048380.1[P],2008-10-1.
[20]刘浩. ABB板形测量系统在宝钢1800mm冷连轧机组的应用[J].冶金自动化,2006,(1):61-62.
[21] Willems, Stefan. S6-high stand for in-line and tandem application [J]. MPT metallurgical plantand technology international,2010,33(3):34-37.
[22]张华. SUNDWIG板型仪系统在冷轧机中的应用[J].可编程控制器与工厂自动化,2010,(8):58-60.
[23] Reinen A.G., Van der Poel, A.A. Preliminary results of the first tinplate mill vidimon-vidiplanshape control system[J]. Steel times,1983,211(11):575-576.
[24]王启顺.空气轴承式板形仪的常见故障及对策[J].轻合金加工技术,2003,31(7):13-16.
[25] Cook C.W., Ames E.S. Borehole-inclusion stressmeter measurements in bedded stalt[J]. U.S.symposium on rock mechanics,1979:481-489.
[26] Jaworski G.w., Dorwart B.C., White W.F., etc.. Behavior of a rigid inclusion stressmeter in ananisotropic stress field[C]. Proceedings-Symposium on rock mechanics,1982:211-218.
[27] Liu Yu-li, JIN Xiao-guang, Lian Jia-chuang. Elastic plastic3D deformation and stress analysis ofstrip rolling [J]. Journal of iron and steel research international,1998,5(2):29-34.
[28]谢云鹏.板形检测与板形控制研究[D].西安：西安理工大学工学硕士学位论文,2002:18-19.
[29]于丙强.整辊智能型冷轧带钢板形仪研制及工业应用[D].秦皇岛：燕山大学工学工学博士学位论文,2010:16-17,31-32.
[30]刘佳伟,张殿华,王鹏飞,等.板形测量辊输出信号处理方法的研究与应用[J].钢铁研究学报,2010,22(2):59-62.
[31]杨景明,王振臣.板形检测辊输出信号的通道处理方法[J].自动化仪表,1997,18(5):34-37.
[32] Neuschutz E, Nucke G, Thies H. New generation BFI flatness measuring system [J]. Metall. planttechnol. Int.,1994,17(1):86-88.
[33]何海涛.900HC冷轧机板形仪测控系统的研制[J].计算机测量与控制,2003,11(7):496-497,518.
[34]杨铁林.高精度气动板形仪基础技术的研究[D].秦皇岛：燕山大学工学博士学位论文,2001:59-60.
[35]刘健.气动板形仪测控系统实验研究及板形理论建模仿真[D].秦皇岛：燕山大学工学博士学位论文,2008:56-57.
[36]刘宏民.板形理论的研究现状与发展方向[J].钢铁,1993,28(2):75-78.
[37]王文明,钟掘,谭建平.板形控制理论与技术进展[J].矿冶工程,2001,21(4):70-72.
[38]刘宏民.三维轧制理论及其应用—模拟轧制过程的条元法[M].北京:科学出版社,1999:57-68.
[39]彭艳,刘宏民,胡建平,等.冷轧带材板形分析及预设定控制软件(FAPCSR)及其应用[J].钢铁,2003,38(2):34-37.
[40]刘宏民,丁开荣,李兴东,等.板形标准曲线的理论计算方法[J].机械工程学报,2008,44(8):137-142.
[41]王东城.基于机理的带钢连轧机板形在线设定控制模型研究[D].秦皇岛：燕山大学工学博士学位论文,2008:4-14.
[42]林振波,张波,连家创,等.冷轧带材板形判别模型的分析与讨论[J].钢铁,1995,30(8):39-43.
[43] Lian Jiachuang, et al.. Study on the large deflection buckling deformation for rolled strip losingstability[C]. Proc. Int. Conf. on steel rolling. Deauville, France,1987, E5:1-7.
[44] Takashi Ishikawa, et al.. Analytical approach to occurrence and suppression of flatness defect instrip rolling[C]. Proc. Int. Conf. on steel rolling. Deauville, France,1987, E7:1-10.
[45]杨荃.冷轧带钢翘曲形状分析与浪形函数[J].钢铁,1993,28(6):41-45.
[46]刘宏民,彭艳,褚宇鹏.板带轧制板形判别的条元法[J].燕山大学学报,2002,26(2):95-98.
[47] Liu Hongmin,Peng Yan,Chu Yupeng,Wang Yingrui.Strip element method for shape discrimitionof strip rolling[C].Communications in numerical methods in engineering,2004,20(9):709-720.
[48]孙亚波,刘宏民,彭艳.板带轧制板形判别的降阶模型[J].工程力学,2009,26(12):204-210.
[49]华建新,周泽雁.冷轧带钢板形缺陷的多项式回归及数学模型[J].钢铁,1992,27(3):27-31.
[50]刘进.冷轧带钢板形缺陷表达式回归及数学模型[J].轧钢,1996,(5):5-9.
[51] Carlstedt A.G., Keijse O.. Modern approach to flatness measurement and control in cold rolling[J]. Iron and steel engineer,1991,(4):34-39.
[52]邸洪双,张晓峰,刘相华,等.冷轧薄带板形检测信号正交多项式分解及数学模型[J].钢铁,1995,3(9):33-36.
[53]周旭东,王国栋.冷轧板形正交多项式分解模型[J].钢铁,1997,32(8):46-47.
[54] Ikuya Hoshino, Masateru Kawai, Misao Kekubo, et al. Observer-based multivariable flatnesscontrol of the cold rolling mill[C].12th World international federation of automatic control,1993,(6):149-156.
[55]彭艳.基于条元法的HC冷轧机板形预设定控制理论研究及工业应用[D].秦皇岛：燕山大学工学博士学位论文,2000:60-65.
[56]单修迎,刘宏民,贾春玉.含有三次板形的新型板形模式识别方法[J].钢铁,2010,45(8):56-60.
[57]黄文俊. BP神经网络在板形缺陷识别中的应用[D].无锡：江南大学工学硕士学位论文,2008:8-9.
[58]刘宏民,单修迎,贾春玉.智能方法在板形控制中的应用[J].燕山大学学报,2010,34(1):1-5,7
[59] Willis M J,Montague G A,Di Massimo C,et al.. Artificial neural networks in process estimationand control[J].Automatica,1992,28(6):1181-1187.
[60] Peng Yan, Liu Hongmin. A neural network recognition method of shape pattern[J]. Journal ofiron and steel reseach international,2001,8(1):16-20.
[61]刘建,王益群,孙福,等.基于粒子群理论的板形模糊模式识别方法[J].机械工程学报,2008,44(1):173-178.
[62] Jia Chunyu, Shan Xiuying, Liu Hongmin, et al. Fuzzy neural model for flatness patternrecognition[J]. Journal of iron and steel research, International,2008,15(6):33-38.
[63]赵小燕,张朝晖,蓝金辉.基于二叉树型分层BP模型的板形模式识别[J].北京科技大学学报,2009,31(2):261-265.
[64]张秀玲,张志强.DHNN优化设计新方法及在板形模式识别的应用[J].智能系统学报,2008,3(3):250-253.
[65]张秀玲,陈丽杰,季颖,等.基于径向函数神经网络的板形模式识别研究[J].工业仪表与自动化装置,2009,(3):7-9.
[66]张秀玲,逄宗鹏,李少清,等.基于自适应神经模糊推理系统的板形模式识别[J].钢铁研究学报,2009,21(9):59-62.
[67]张秀玲,李少青,田力勇. Elman神经网络的板形模式识别方法[J].智能系统学报,2010,5(5):449-453.
[68] Yu Bingqiang, Sun Yabo, Liu Hongmin, etc.. Compensation model for shape measuring of coldstrip rolling [J]. Journal of iron and steel research, International,2010,17(6):21-26.
[69] Rubén Usamentiaga, Daniel F. García. Compensation for uneven temperature in flatness controlsystems for steel strips[C]. Conference record of the2006IEEE,2006,1:521-528.
[70] Peng Yan, Liu Hong-min. Coupling solved roll contact pressure transverse distribution of HCstrip mill[J].Trans.Nonferrous Met.Soc.Chian,2001,11(6):891-894.
[71] Jia-wei Liu, Dian-hua Zhang, Jun-sheng Wang. Deflection compensation model for flatnessmeasuring roll[J].Journal of iron and steel research, International,2010,17(12):35-37.
[72]杨荃,陈先霖.冷轧机的板形控制目标模型[J].北京科技大学学报,1995,17(3):254-258.
[73]林振波,段振勇,连家创,等.冷轧机板形标准曲线的分析及选择方法[J].钢铁研究学报,1997,19(3):55-58.
[74]贾春玉,尚志东.冷轧板形目标曲线的补偿设定[J].钢铁研究学报,2000,12(4):64-67.
[75]白剑,张清东,常铁柱,等.1420冷轧机组第1机架板形控制目标曲线研究[J].钢铁,2007,42(10):56-59.
[76]王鹏飞,张殿华,刘佳伟,等.冷轧板形目标曲线设定模型的研究与应用[J].钢铁,2010,45(4):50-55.
[77] T.Furuya, et al. Neww design6-high cold mill (HC mill) solves shape problem[G]. AISE yearbook,1979:187-282.
[78]张清东.宽带钢冷轧机板形自动控制系统的研究[D].北京:北京科技大学工学博士学位论文,1994:15-25.
[79]张云鹏.宽带钢冷轧机板形控制效应函数研究[D].北京:北京科技大学工学博士学位论文,1997:10-13.
[80]彭艳. HC轧机板形控制模型研究[D].秦皇岛:燕山大学工学硕士学位论文,1997:4-9.
[81]张清东,陈先霖,何安瑞,等.冷轧宽带钢板形检测与自动控制[J].钢铁,1999,34(10):69-72.
[82]丁修堃.轧制过程自动化[M].第2版.北京:冶金工业出版社,2009:268-275,233.
[83] Teruhiro Saito, Takeo Ohnishi, Tomio Komatsu. Automatic flatness control system in tandemcold rolling mill for ultra-thin gauge strip [J]. Kawasaki steel technical,1991,(24):41-46.
[84] Davies, W.E., Sivilotti, O.G., Tulett, M.W. Production and control of strip flatness in coldrolling [J]. Metals technology,1975,(10):494-498.
[85] Roger Davidson. Automatic flatness control system[J].Iron and steel engineer,1986,(10):44-50.
[86]林振波,连家创,刘宏民. ABB板形测量和控制系统及板形标准曲线[J].轧钢,1994,(4):20-24.
[87] Cfaire NaPPez, SergeBoulot, Richard C MeDermott. Control of strip flatness in cold rolling: aglobal approach[J].Iron and steel engineer,1997(4):42-45.
[88] Sean J. Egan..Flatness Modelling and control for a continuous tandem cold mill [J]. Iron andsteel engineer,1996,(3):38-41.
[89]王长松,张清东,张云鹏.效应函数在冷轧机板形控制中的应用[J].轧钢,1999,(4):28-30.
[90]张云鹏,张清东,王长松.基于效应函数的冷轧机板形闭环控制策略[J].北京科技大学学报,1999,21(2):195-197.
[91] Peng Yan,Liu Hongmin, R.Du. A neural network-based shape control system for cold rollingoperations [J]. Journal of materials processing technology,2008,(202):54-60.
[92] H.T Zhu, Z.Y Jiang, A.K Tieu, G.D Wang.A fuzzy algorithm for flatness control in hot strip mill[J]. Journal of materials processing technology,2003,140(1-3):123-128.
[93] Carlos Thadeu de Auila Pire, Henrique Cezar Ferreira, Reberto Moura Sales. Adaptation fortandem cold mill models[J]. Journal of materials processing technology,2009,209:3592-3596.
[94] Luis E. Zárate, Fabrício R. Bittencout. Representation and control of the cold rolling processthrough artificial neural networks via sensitivity factors[J]. Journal of materials processingtechnology,2008,197(1-3):344-362.
[95]张秀玲.冷带轧机板形智能识别与智能控制研究[D].秦皇岛:燕山大学工学博士学位论文,2002:87-99.
[96]张秀玲,刘宏民.板形控制的传递矩阵方法[J].机械工程学报,2003,39(11):100-103.
[97] Liu Hongmin, Zhang Xiuling, Wang Yingrui. Transfer matrix method of flatness control for stripmills[J]. Journal of materials processing technology,2005,166(2):237-242.
[98] Liu Hong-Min, Zhang Xiu-Ling, Yang Li-Po. Matrix equation and its applications of flatnesscontrol for strip mills[C]. AISTech2006-Iron and steel technology conference proceedings,2006,2:97-108.
[99]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:燕山大学工学博士学位论文,2008:41-75.
[100] Liu Hongmin, He Haitao, Shan Xiuying, ect. Flatness control based on dynamic effective matrixfor cold strip mills [J]. Chinese journal of mechanical engineering,2009,22(2):1-10.
[101]单修迎.基于机理-智能的带钢冷轧机高精度板形控制研究[D].秦皇岛:燕山大学工学博士学位论文,2010:11-33,36-63.
[102]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,2008,5(4):360-365.
[103] LI ZM, Kong LF.New modeling method of soft sensor based on support vector set-RBF neuralnetwork[C]. International conference on informational technology and environmental systemScience,2008,2:1151-1156.
[104]张清东,李博,郑武,等.冷轧机板形闭环反馈控制策略及模型研究.系统仿真学报,2009,21(24):7858-7862.
[105]王崇涛,杨荃,彭鹏,等.宽带钢冷连轧机动态板形控制策略[J].北京科技大学学报,2009,31(4):492-498.
[106] Alberto Bemporad, Daniele Bernardini, Francesco Alessandro Cuzzola, etc. Optimization-basedautomatic flatness control in cold tandem rolling [J]. Journal of process control,2010,20(4):396-407.
[107]曹忠华,朱文豪,李志锋,等.冷轧机板形分段冷却控制系统分析[J].鞍钢技术,2010,(6):31-34.
[108] Mohammad Abbaspour, Ahmad Saboonchi. Work roll thermal expansion control in hot stripmill[J]. Applied mathematical modelling,2008,32(12):2652-2669.
[109] A. Saboonchi, M. Abbaspour. Changing the geometry of water spray on milling roll and its effecton the work roll temperature [C]. Process. Technol,2004,(148):35–49.
[110] Shohet K N, Townsend N A. Roll bending methods of crown control in four-high plate mills[J].Journal of the iron and steel institute,1968,206(11):1088-1098.
[111] Xin-yi Ren, Feng-shan Du, Hua-gui Huang. Application of improved fuzzy immune PIDcontroller to bending control system [J]. Journal of iron and steel research, International,2011,18(3):28-33.
[112] Yin Guo-fang, Wang Yi-qun, Sun Xu-guang. Predictive control of electro-hydraulic bendingcontrol system[J]. Hydraulics pneumatics and seals,2003,(4):21-26.
[113] R-Min Guo. Characteristics of rolling mills with roll shifting[J].Iron and steel engineer,1988,(12):45-52.
[114] J.S. Wang, Z.Y. Jiang, A.K. Tieu, X.H. Liu, etc. Analysis of thrust force in a work roll shiftingmill[J]. International journal of mechanical sciences,2006,48(10):1095-1102.
[115]孙蓟泉,周永红.板形控制技术及应用[J].鞍钢技术,2006,(4):6-11.
[116] Klamma K. CVC technology in cold rolling mills[J]. Metallurgical plant and technology,1985,8(3):60-62;66-67.
[117] Bald W, Beisemann G, Feldmann H, etc. Continuously variable crown(CVC) rolling[J]. Iron andsteel engineer,1987,64(3):32-41.
[118] Nakajima Koei, Kawamoto Toshiharu, Hatae Seiryo. Basic characteristics of pair cross mill[J].Technical review-Mitsubishi heavy industries,1985,22(2):143-148.
[119] Kamada Shunji, Iyama Shoichi, Hamada Ryuji. Edge profile control using pair cross mill in coldrolling [J]. Iron and steel engineer,1996,73(6):20-26.
[120] Hrioshi Aralta,Tsuneo Nakano.Expansion of pair cross(PC) mill applications to hot and coldrolling mills [J].Iron and Engineer,1993,(3):32-37.
[121] Wang B.W., Hu B.J.. Study of rolling force distribution along the rolling of pair cross mill [J].Journal of university of science and eechnology beijing Mineral metallurgy materials (Eng Ed),1996,3(2):107-113.
[122] R G Parker,P J Sathe. Free vibration and stability of aspinning disk-spindle system [J]. Journal ofvibration and acoustics,1999,121:391-396.
[123] J Kipkhope, G J Wilson. Vibration and stress analysis of the rotating discs using annular finiteelements[J].Journal of sound and vibration,1976,44(4):461-474.
[124]肖宏,谢红飙,张国民. PC轧机轧制过程耦合数值模拟研究[J].工程力学,2005,22(3):216-219,215.
[125] Nagai Toshihiko, Yamada Junzo, Masui Takeshi. Strip shape and profile control with sumitomovariable crown roll system [J]. Iron and steel engineer,1983,60(1):56-63.
[126] Masui, T., Nagai, T., Takigawa, T.. Current applications of sumitomo variable crown roll systemto ferrous and non-ferrous metal strip mills [J]. Inst of metals,1985:143-151.
[127] Anon. Shape control in cold rolling by combining variable crown and roll bending [J]. Timesinternational,1997,(3):34-40.
[128]白振华,连家创. VC轧机板形控制关键技术的开发与研究[J].中国机械工程,2003,14(15):1287-1289.
[129] Becker, B., Legrand, N., Vallet, D.,etc.. DSR technology to improve strip flatness on a steeltandem cold mill [J]. Revue de metallurgie. Cahiers D'Informations techniques,2000,97(6):799-813.
[130] Wang, C.W., Perret, G., Goudet, J.,etc.. Installation of the first DSR for steel application atbaosteel: First results [J]. Revue de Metallurgie. Cahiers D'Informations Techniques,1998,95(10):1287-1297.
[131] Wang, J.-F., Jao, L., Faure, J.-P..Operational results of the DSR installed on the No.1tandemcold mill of baoshan iron and steel[J]. Revue de Metallurgie. Cahiers D'Informations Techniques,2005,102(3):241-249.
[132]张清东,王文广,周晓敏. DSR宽带钢冷轧机的特殊板形控制性能[J].北京科技大学学报,2008,30(1):71-76.
[133]单成祥.传感器的理论与设计基础[M].北京:国防工业出版社,1999:311-326.
[134]张国忠,赵家贵.检测技术[M].北京:中国计量出版社,1998:363-378.
[135]安宗权,汪云祥. PCB排版对提高单片机应用系统可靠性的研究[J].安徽电机学院学报,2002,17(3):34-37.
[136]张清东,黄纶伟,周晓敏.宽带钢轧机板形控制技术比较研究[J].北京科技大学学报,2000,22(2):177-181.
[137]徐乐江.板带冷轧机板形控制与机型选择[M].北京:冶金工业出版社,2007:164-177.
[138]李连平,赵志毅,孙彤彤,等.热轧薄带钢板形影响因素的离线模拟分析[J].钢铁,2006,41(7):49-53.
[139]张小平,郭会光,张雪娜,等.板宽对板形的实验研究[J].太原科技大学学报,2009,30(3):225-227.
[140]张雅琴,张小平,郭会光,等.冷轧带钢残余应力及其对板形影响的研究[J].塑性工程学报,2009,16(5):75-79.
[141]张小平,张少琴,何宗霖,等.张力对板形影响的实验研究[J].太原科技大学学报,2009,30(4):312-315.
[142]马超,张国强,赵德华,等.弯辊力对带钢板形的模拟研究[J].鞍钢技术,2009,(5):36-40.
[143] Vladimir B.Ginzburg. High-quality steel rolling: theory and practice[M].New york: MarcelDekker,Inc.,2000:349-352,366.
[144]杨代纶.基于质心的二维和三维插值算法[J].计算机工程与应用,2004,(12):77-78.
[145]杨代纶,勾成俊,唐志全,等.矩阵模型光子束计量算法的改进[J].核技术,2004,27(3):222-227.