基于影响矩阵自学习的板形闭环控制方法  

Automatic fatness control method based on self-learning of effective matrix

作　　者：李志明[1,2] 彭艳[1] 刘宏民[1] 

机构地区：[1]燕山大学轧制设备及成套技术教育部工程研究中心,秦皇岛066004 [2]燕山大学信息科学与工程学院,秦皇岛066004

出　　处：《塑性工程学报》2011年第2期45-51,共7页Journal of Plasticity Engineering

基　　金：国家自然科学基金资助项目(50675186);河北省重大自然科学基金资助项目(E2006001038)

摘　　要：文章在板形控制影响矩阵理论框架的基础上,通过确定板形调控机构的关键影响因素,建立不同板带材质、不同道次的影响矩阵先验值表。在板形闭环控制过程中,影响矩阵先验值表利用实测板形数据以自学习的方式不断地改善自身品质,使其与板形调控机构的实际调控效能更加接近。由于自学习过程是在各种调控机构调控性能影响因素实际耦合作用的情况下进行的,从某种意义上讲,在求解影响矩阵时,该方法较智能方法考虑的因素更加全面,为提高板形控制精度奠定了一定的基础。此外,影响矩阵的计算及影响矩阵的自学习均采用简单的数学算法实现,计算速度快,实时性能好。仿真研究表明,该方法具有控制精度高、稳定性好、适应能力强等特点,适合在线应用,而且便于实施。On the basis of the theoretical frame of effective matrix method for flatness control,the empirical value tables of effective matrix on different strip material and rolling pass by ascertaining the key effective factors of flatness control mechanism.In the process of flatness closed-circle control,the effective matrix empirical value table,making the best of measured flatness data,improves ceaselessly the quality of itself by self-learning in order to approach the actual flatness control efficiency.The self-learning process was under the coupled action of all kinds of flatness actuators,therefore,the factors considered are more complete than the artificial intelligence method for computing the effective matrix and this lay the foundations for improving the control precision of flatness.The computing and self-learning processes are all realized by simple mathematical algorithms with high computing speed and is suitable for real-time performance.The simulation results show that this automatic flatness control method is featured in high control precision,good stability,high adaptability and is suitable for on-line applications.

关 键 词：板形控制 影响矩阵 先验值表 自学习 

分 类 号：TG335.5[金属学及工艺—金属压力加工] TP273.2[自动化与计算机技术—检测技术与自动化装置]