Roll eccentricity compensation control based on iterative learning
QIAO Ji-zhu1, SHI Hong-jian2, SUN Jie1, PENG Wen1, LI Xiao-jian3, ZHANG Dian-hua1
1. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China; 2. Equipment Department, Ningbo Baoxin Stainless Steel Co., Ltd., Ningbo 315800, Zhejiang, China; 3. College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
Abstract：Aiming at roll eccentricity in rolling mill, an iterative learning control method which was widely used in industrial robot repeatability control was applied to roll eccentricity compensation control by combining the characteristics of eccentricity signal and compensation control method. On the basis of learning the cycle error data, the adjustment of rolling mill eccentricity control system was more accurate. The test results of constructed roller eccentricity signal show that compared with the fast Fourier transform compensation method, the compensation accuracy is improved by 20.1%-56.4% after 10-50 iteration learning. After iterative learning law parameters are optimized, the compensation accuracy reaches more than 50% after iterative learning, and the convergence speed is greatly improved. Considering that the frequency of eccentricity signal changed when the rolling mill was accelerating and decelerating, the frequency conversion signal was compensated by iterative learning control. After 10 times of fast learning compensation, the value of error evaluation function reached 13.6% of initial. The actual production line data of one pass in continuous cold rolling are used for testing, and the results show that the iterative learning compensation control can effectively improve the accuracy of eccentricity compensation after learning. Study results based on the iterative learning control method provides some reference for the compensation control of roll eccentricity.
乔继柱, 史鸿剑, 孙杰, 彭文, 李霄剑, 张殿华. 基于迭代学习的轧辊偏心补偿控制[J]. 中国冶金, 2023, 33(5): 102-108.
QIAO Ji-zhu, SHI Hong-jian, SUN Jie, PENG Wen, LI Xiao-jian, ZHANG Dian-hua. Roll eccentricity compensation control based on iterative learning[J]. China Metallurgy, 2023, 33(5): 102-108.
YANG Z, LIU D, ZHENG G, et al. Roll eccentricity repetitive compensation control based on sparse Fourier transform for cold rolling mill[C]// Proceedings of 2018 Chinese Automation Congress (CAC). Xi’an: Chinese Association of Automation, 2018:3232.
Norrlof M. An adaptive iterative learning control algorithm with experiments on an industrial robot[J]. IEEE Transactions on Robotics and Automation, 2002, 18(2):245.
[12]
Kim D I, Kim S. An iterative learning control method with application for CNC machine tools[J]. IEEE Transactions on Industry Applications, 1996, 32(1): 66.
[13]
Chen Shu-yang, Wang Zhi-gang, Chakraborty A, et al. Robotic deep rolling with iterative learning motion and force control[J]. IEEE Robotics and Automation Letters, 2020, 5(4):5581.
[14]
Browne F, Rees B, Chiu G, et al. Iterative learning control with time-delay compensation: An application to twin-roll strip casting[J]. IEEE Transactions on Control Systems Technology, 2021, 29(1):140.
[15]
Uchiyama M. Formation of high speed motion pattern of a mechanical arm by trial[J]. Transactions of the Society of Instrument and Control Engineer, 1978, 14(6):706.
[16]
Arimoro S, Kawamura S, Miyazaki F. Bettering operation of robots by learning[J]. Journal of Robotic Systems,1984,1(2):123.
Amann N,Owens D H. Iterative learning control using optimal feedback and feed forward action[J].International Journal of control,1996.65(2):277.
[20]
Owens D H, Feng K. Parameter optimization in iterative learning control[J]. International Journal of control, 2003,76(11): 1059.
[21]
Hatonen J, Owens D H, Feng K. Basis functions and parameter optimization in high-order iterative learning control[J]. Automatic, 2006, 42(2): 287.
[22]
逄勃,邵诚.高阶参数优化迭代学习控制算法[J].控制理论与应用,2015,32(4):561.
[23]
Owens D H, Hatonen J, Daley S. Robust monotone gradient-based discrete-time iterative learning control[J]. International Journal of Robust and Nonlinear Control, 2009, 19(6): 634.
2023, Vol. 33 
