Optimal multi-variable flatness control for a cold rolling mill based on a box-constraint optimisation algorithm

As the flatness control system is a multi-variable control system, the key issue for high-precision flatness is the determination of the optimal adjustments of flatness actuators. In order to determine these the first step is the establishment of a multi-variable control model in the flatness control process, by which the actual flatness control problem has been reduced to a non-linear optimisation problem with box-constraints. Around this optimisation problem, characteristics and applicability of current optimisation algorithms were analysed. Based on the coordinate descent method, a new multi-variable optimisation algorithm with global convergence was proposed. In the algorithm, the problem was transformed into a series of univariate optimisations which could be solved by sequentially searching along coordinate directions. In order to make the objective function decrease rapidly, and ensure that each iteration are carried out within the feasible region, the accelerating step method is adopted to determine the iterative step size. A series of feasible points have been obtained through a series of iterations, which make the objective function decrease gradually until the optimal solution has been obtained. Finally, the algorithm has been tested by numerical experiments with production data of actual flatness control process, and applied to the 1450?mm tandem cold mill. Numerical experiments and application show that the proposed algorithm not only can satisfy the requirements of response speed, but also have a good accuracy, which can provide a reference for the realisation of high-precision flatness control processes of cold rolled strip.     

六辊冷连轧机板形前馈控制方法的研究

 本文针对六辊冷连轧机,研究了采用工作辊弯辊的整体补偿板形前馈控制方法和采用工作辊与中间辊弯辊相结合的最优综合补偿板形前馈控制方法。实际控制效果表明,整体补偿板形前馈控制方法只能抑制由于轧制力波动产生的二次板形,而对四次板形几乎不产生影响;最优综合补偿板形前馈控制方法对轧制力波动造成的二次和四次板形均具有很强的控制能力。     

基于效应函数的冷轧机板形闭环控制策略

对效应函数和冷轧板形调节性能进行界定.在此基础上提出了基于效应函数的板形闭环控制策略,揭示了该控制策略内含的数学模型,并对其控制效果进行了分析和评价.     

Research and Application of Dynamic Substitution Control of Actuators in Flatness Control of Cold Rolling Mill

The work roll bending control has a strong ability to eliminate the symmetrical flatness defects of strip. However, if the incoming strip is coming with severe symmetrical flatness defects, it happens that the position limit of work roll bending is prone to be reached or exceeded during the execution of the displacement, resulting in the residual symmetrical flatness defects without further elimination, as well as a limited flatness control process. Abilities of work roll bending and intermediate roll bending / shifting for flatness control have been analyzed based on the actuator efficiencies for the purpose of solving the problem of work roll bending control reaching threshold. Meanwhile, a self‐learning determination model of actuator efficiency factors, which can be utilized to determine the actuator efficiency factors online accurately, was developed from the measurement data extracted from the rolling mill. Models of intermediate roll bending and intermediate roll shifting substitution control in case of work roll bending control over‐limitation have been developed, in accordance with the practical conditions and the degree of work roll bending over‐limitation. Applications show that the substitution control can play an important role in the residual symmetrical flatness deviation control, which should have been removed completely by the work roll bending control.     

基于轧前凸度信息的冷连轧机板形平坦度前馈控制方法

提出了基于冷轧前带钢板凸度在线实测信息的板形平坦度前馈控制方法,建立了冷连轧板形平坦度预测控制模型,通过与实际值比较验证了其正确性,并对板形平坦度前馈控制策略和板形平坦度前馈控制模型进行了研究.在此基础上以国内某条在轧机入口装备有板廓检测仪的1550mm五机架六辊UCMW冷连轧机组为对象,建立了一套完整的板形平坦度前馈控制模型和离线仿真系统.仿真结果表明,在前馈控制模型投入的情况下,由于来料带钢板凸度变化所造成的成品带钢板形平坦度波动明显减弱,达到了进一步提高成品带钢板形质量的目的.     

A Novel Method for Flatness Pattern Recognition via Least Squares Support Vector Regression

 To adapt to the new requirement of the developing flatness control theory and technology, cubic patterns were introduced on the basis of the traditional linear, quadratic and quartic flatness basic patterns. Linear, quadratic, cubic and quartic Legendre orthogonal polynomials were adopted to express the flatness basic patterns. In order to overcome the defects live in the existent recognition methods based on fuzzy, neural network and support vector regression (SVR) theory, a novel flatness pattern recognition method based on least squares support vector regression (LS-SVR) was proposed. On this basis, for the purpose of determining the hyper-parameters of LS-SVR effectively and enhancing the recognition accuracy and generalization performance of the model, particle swarm optimization algorithm with leave-one-out (LOO) error as fitness function was adopted. To overcome the disadvantage of high computational complexity of naive cross-validation algorithm, a novel fast cross-validation algorithm was introduced to calculate the LOO error of LS-SVR. Results of experiments on flatness data calculated by theory and a 900HC cold-rolling mill practically measured flatness signals demonstrate that the proposed approach can distinguish the types and define the magnitudes of the flatness defects effectively with high accuracy, high speed and strong generalization ability.     

A novel approach to improve the computing accuracy of rolling force and forward slip

In cold strip rolling control system, rolling force and forward slip are the prerequisites for the model setting calculation, and the deformation resistance and friction coefficient are the main parameters that affect their predictions. A new method based on objective function is first proposed in this paper to improve the calculation accuracy of rolling force and forward slip, and the deformation resistance and friction coefficient are taken as optimisation variables. Using the multi-population co-evolutionary algorithm to solve the objective function, the required rolling force and forward slip are obtained. The pre-set values of rolling force and roller line speed are compared with the actual measured ones in a 1450?mm five-stand tandem cold mill and other researcher’s method. Results show that the calculated values are in fair agreements with the on-line measured ones, and the thickness and flatness accuracy of the final product are improved.     

Development and application of cold strip rolling flatness control system in Ansteel

In this paper,the flatness control technology AnShaper^TM for cold-rolling mill and industry application are introduced.AnShaper^TM includes;partitioning piezomagnetic shape meter for flatness measurement for cold-rolling strip;flatness measured signals processing system based on digital signal processing（DSP）;flatness feedback control model system based on the control efficiency of flatness control actuators and model adaptive function.The application verifies that strip flatness can meet the need of high quality product by using AnShaperTM.The average flatness quality is about 5Ⅰ-unit and the 0.18 mm ultrathin thickness strip flatness is 10Ⅰ-unit.     

板形闭环控制系统的最优化算法

为了快速、精确地确定轧机的板形调节机构的最优调节量,实现最大限度地消除带钢的在线板形偏差,建立了一种以板形调控功效为基础的板形闭环控制新方法,即矩阵分析法。矩阵分析法是将板形调节机构的多目标评价函数转化为标准二次型函数,分别求取m个变量在可行域内绝对值最小对应的值,经过矩阵逆变换求得执行机构的最佳调节量,以达到对评价函数进行可行域内最小值求解。为了验证矩阵分析法的正确性,分别将最小二乘法和矩阵分析法应用于1450冷连轧机系统的板形调节机构的最优调节量求解。计算结果表明:在执行机构调节量超范围情况下,与最小二乘法相比,矩阵分析法可以求得全域第二最优解,实现了对板形的最优控制。     

超宽冷轧机带钢板形特征聚类分析

为准确掌握超宽冷轧机不同宽度带钢的板形特征,以某2180 mm超宽冷轧机1900 mm宽度带钢实测板形数据为研究对象,借鉴‘大数据’的思想,结合数据挖掘领域中聚类分析方法,提出基于网格和密度的板形特征聚类方法,并以此方法对几种典型带钢宽度的大量板形实测数据进行分析,得到不同宽度带钢的板形特征.以分段函数对板形特征进行多项式表达,得到不同宽度带钢的板形特征参数化分析结果.提出的基于网格和密度的板形特征聚类与分析方法,能够快速准确地对大量板形实测数据进行分析,提取出长期生产过程中板形缺陷特征并得到参数化表达,从而为冷连轧机,特别是超宽带钢冷连轧机的辊形改进和控制策略优化提供数据基础.     

Roll Subsectional Cooling Adaptive Fuzzy Control Based on Fuzzy Model Inversion

 Flatness is an important equality indicator of strip rolling and roll sub-sectional cooling is an important method for flatness control, especially for high order flatness component control. It is very hard to build the mathematic model of roll sub-sectional cooling because of its characteristics of nonlinearity, hysteresis quality and strong coupling etc. In order to improve the control effect of roll sub-sectional cooling control model, the roll sub-sectional cooling adaptive fuzzy control model based on fuzzy model inversion is built according to the separation principle of fuzzy form on the basis of the conventional fuzzy control model, where the parameters of the fuzzy controller can be dynamically regulated according to the change of rolling conditions. Simulation experiment results of the model indicate that the proposed roll sub-sectional cooling adaptive fuzzy control model based on fuzzy model inversion has high control precision and rapid response speed with strong self-learning and anti-interference capacity and a new method is provided for high-precision flatness control.     

UCMW冷连轧机组各架中间辊端部辊形

针对UCMW冷带轧机板形控制中存在的两类耦合问题——边降与平坦度的控制耦合以及工作辊弯辊与中间辊弯辊的调控功效耦合,定义了描述耦合度的参数以量化其耦合程度,提出通过设计中间辊端部辊形实现对两类耦合问题的解耦思路,并以某1550UCMW冷连轧机为对象进行了中间辊端部辊形设计.仿真计算结果表明,利用所设计的中间辊端部辊形可以在一定程度上化解两类耦合问题的耦合关系,且可以进一步优化改善轧机的板形控制性能,达到提高实物板形质量的目的.     

基于有限元与神经网络的板形调控功效

 针对冷连轧机组中的6辊CVC轧机,利用大型非线性有限元软件MSC.Marc建立仿真模型,对多种轧制条件下轧件的弹塑性变形和辊系的弹性变形进行了耦合计算,得到了6辊CVC冷轧机工作辊弯辊、中间辊弯辊、中间辊横移的板形调控功效离散值。研究了不同轧制工艺参数、轧件参数和轧辊参数对各板形调节机构调控功效的影响规律,并得出相应的板形调控功效系数。以有限元计算结果为样本,利用BP神经网络强大的非线性映射功能,建立了板形调控功效的神经网络计算模型,为板形在线闭环控制模型提供高精度的板形调控功效,解决了有限元计算耗时长,难以满足在线控制要求的问题。     

基于模糊推理的板形自动控制算法及其应用

 为了提高带钢板形质量,开发了板形自动控制系统应用于连退平整机上。该控制系统使用了基于模糊推理的板形自动控制算法,算法根据板形偏差的大小及变化趋势在线整定增益系数。当板形偏差过大或变化过快时,该算法能快速将板形调整到目标值,从而能确保板形偏差始终在较小范围内。目前,算法已经应用在宝钢2条连续退火机组上,应用效果良好。     

板形模式识别的GA-BP模型和改进的最小二乘法

针对板宽变化时需要不同拓扑结构的神经网络才能完成板形模式识别任务，网络学习工作量大，网络存在收敛速度慢，易陷入局部极小等结构性能不佳的问题，首次建立了以勒让德正交多项式为基模式的只用6个输入信号、3个输出信号的板形模式识别GA-BP网络模型。该模型不仅结构简单，而且物理意义明确，识别精度较高，解决了板宽变化时神经网络结构形式不变的问题，从而实现了板形模式识别的智能化。又提出了基于勒让德正交多项式的板形模式识别最小二乘法，该方法简单、实用，识别精度较高，克服了传统的最小二乘模型板形模式识别的缺点和不足。为板形模式识别提供了两种简便实用的新方法，发展了板形模式识别理论和方法。     

Fuzzy Neural Model for Flatness Pattern Recognition

For the problems occurring in a least square method model, a fuzzy model, and a neural network model for flatness pattern recognition, a fuzzy neural network model for flatness pattern recognition with only three-input and three output signals was proposed with Legendre orthodoxy polynomial as basic pattern, based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm. The model not only had definite physical meanings in its inner nodes, but also had strong self-adaptability, anti interference ability, high recognition precision, and high velocity, thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient, practical, and novel method for flatness pattern recognition.     

整辊内嵌式板形仪挠度干扰信号的消除方法

 板形测控技术是生产高端冷轧带钢的必备技术,针对国产板形测控系统研发过程中遇到的一系列新的科学技术问题,研究了如何消除整辊内嵌式板形仪挠度引起的干扰信号。首先,分析了整辊内嵌式板形仪结构特点与传感器连接型式的特殊性;然后,分析了板形信号的理想波形与实际波形特征,根据其特征提出一种新的挠度干扰信号的消除方法,根据传感器安装位置固定的特点确定干扰信号相位,以有效信号之间波形平直为目标对干扰信号幅值进行优化;最后,通过实例和工业应用对该方法的有效性进行验证。研究结果表明,该方法可以高效精确地消除挠曲附加波形,使径向压力和板形分布变得平滑,有效提高检测精度。     

基于非线性预测模型的单神经元自适应PID板形控制

为了解决传统PID板形控制精度低、速度慢、抗干扰能力差等问题,将BP神经网络和单神经元引入到板形的控制中,提出一种基于BP神经网络预测模型的单神经元自适应PID控制的板形控制策略。利用BP神经网络的非线性逼近能力和单神经元的自学习、自适应能力,通过两者的有机结合寻找一个最佳的P、I、D非线性组合控制律,实现对带钢板形缺陷的有效控制。仿真实验结果表明,该控制算法能很好地跟踪板形的目标设定值,提高了系统的控制精度,加快了系统的响应速度,并且具备较强的抗干扰能力。     

基于CF-PSO-SVM的冷连轧非稳态工作辊弯辊模型优化

 板形质量是冷轧带钢重要的技术质量指标,同时工作辊弯辊是改善冷轧带钢板形质量的最有效的控制手段之一。冷连轧机组在高速稳定的轧制过程中板形控制精度能够达到较高的水平,但在升降速非稳态的轧制过程中板形控制效果非常不理想,这也成为制约冷轧带钢产品质量的不利因素。为了提高冷连轧机在升降速非稳态轧制过程中带钢板形的控制精度,在深入研究了冷连轧弯辊力设定原理的基础上,利用智能算法和包括出入口带钢厚度、机架间口张力、轧制速度、中间辊窜辊、带钢宽度、轧辊倾斜以及轧制力等现场实际轧制大数据样本,提出了一种基于粒子群算法优化支持向量机的工作辊弯辊力预测模型。同时阐明了粒子群优化算法和支持向量机的基本原理,引入压缩因子的概念,提升了粒子群算法参数寻优的效率,选取冷连轧机组五机架为研究对象,利用拉依达准则对轧制数据样本进行处理,通过平均绝对误差、均方差误差和平均绝对误差百分比等评价指标对比预测模型的性能。结果表明,改进的预测模型具有良好的模型预测性能和泛化能力,同时根据实际生产数据样本,回归出基于轧制速度和辊间弹性系数的弯辊力缝补偿模型,并验证了模型的有效性,模型的投入降低了板形控制系统的负荷,改善了非稳态轧制过程中的板形控制精度,产品头尾部的质量合格率提高了5.1%。