摘要
根据铝板带热连轧过程中板带的跑偏运动特点,建立了热连轧铝板带跑偏过程数学模型,分别求得了以辊缝差、来料厚差等跑偏诱因为输入,以轧制力差、跑偏量为输出的跑偏传递函数。建立了多冲量串级纠偏控制系统,并运用预测函数控制作为纠偏系统的控制算法。将纠偏预测函数控制系统运用到热连轧最容易发生跑偏的F4机架中进行纠偏控制数值模拟,并将纠偏控制效果与传统PID控制纠偏效果模拟进行对比。数值模拟结果显示:运用预测函数控制的纠偏控制系统,调整时间为0.5 s,轧制力差超调量小于10 k N,跑偏量超调量小于1 mm;其鲁棒性明显比PID强,调整时间短,超调量小,能够满足高速轧制下的纠偏控制。对纠偏预测函数控制算法进行了实验验证。实验结果显示,纠偏系统在以现场数据为输入和扰动时,能够快速、稳定、准确地减小各机架的轧制力差,防止出现轧制过程中出现大幅的跑偏现象。
For the characteristics of aluminum strip deviation in hot aluminum strip continuous rolling, a deviation mathematical model of hot aluminum strip continuous rolling was set up. The multi-impulse cascade control system was found with deviation factors of roll gap difference and thickness difference as inputs, rolling force difference and quantity of deviation as transfer function, and predictive functional control as control algorithm. A simulation of correcting functional control system was conducted, and it is used in F4 frame, which is the easiest to deviate in the hot aluminum strip continuous rolling. The control results were compared with the traditional PID control. The simulation results show that when the correcting control system is used in the predictive control system, the adjusting time is 0. 5 s, the overshoot of rolling force difference is less than 10 kN, and the overshoot of deviation is less than 1 ram. Its robustness is stronger than that of PID in the short adjusting time, small overshoot, and meets correcting control requirements in high speed rolling. The control algorithm of correcting predictive function was verified by experiment. The experimental results show that when the correct system is regarded as field data to input and disturb, the rolling force difference of any frames quickly can be reduced stably and accurately in case deviation phenomenon during rolling.
出处
《锻压技术》
CAS
CSCD
北大核心
2015年第8期24-29,共6页
Forging & Stamping Technology
基金
国家自然科学基金资助项目(51374241)
河北省高等学校科学技术研究项目(Z2014125)
河北建筑工程学院青年基金资助项目(QN201402)
河北建筑工程学院青年基金资助项目(Q-201308)
关键词
铝板带
热连轧
纠偏
预测函数控制
aluminum strip
hot continuous rolling
deviation guiding
predictive functional control