Abstract
The performance of Smith prediction monitoring automatic gauge control (AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
DYJA H, MARKOWSKI J, STOINSKI D. Asymmetry of the roll gap as a factor improved work of the hydraulic gauge control in the plate rolling mill [J]. Journal of Materials Processing Technology, 1996, 60: 73–80.
PENG Yan, LIU Hong-min, WANG Dong-cheng. Simulation of type selection for 6-high cold tandem mill based on shape control ability [J]. Journal of Central South University of Technology, 2007, 2: 278–284.
YANG Bin-hu, YANG Wei-dong, CHEN Liao-gui, QU Lei. Dynamic optimization of feedforward automatic gauge control based on extended kalman filter [J]. Journal of Iron and Steel Research International, 2008, 15(2): 39–42.
PENG Peng, YANG Quan. Generalized shape and gauge decoupling load distribution optimization based on IGA for tandem cold mill [J]. Journal of Iron and Steel Research, International, 2009, 16(2): 30–34.
LIU Ke-zhi, HE An-rui, YANG Quan, ZHAO Lin, GUO Xiao-bo. Dynamic decoupling for combined shape and gauge control system in wide strip rolling process [J]. Journal of Iron and Steel Research International, 2008, 15(2): 28–31.
SUN Shu-fang, ZHANG Jia-hai, WANG Jian-hui, XU Lin. The application of new adaptive PSO in AGC and AFC combination control system [J]. Procedia Engineering, 2011, 16: 702–707.
KOOFIGAR H R, SHEIKHOLESLAM F, HOSSEINNIA S. Unified gauge-tension control in cold rolling mills: A robust regulation technique [J]. International Journal of Precision Engineering and Manufacturing, 2011, 12(3): 393–403.
WANG Qing-guo, ZOU Biao, ZHANG Yu. Decoupling smith predictor design for multivariable systems with multiple time delays [J]. Institution of Chemical Engineers, 2000, 78 (A): 565–572.
IBRAHIM KAYA. A new smith predictor and controller for control of processes with long dead time [J]. ISA Transactions, 2003, 42: 101–110.
PALMOR Z J, BLAU M. An auto tuner for Smith dead-time compensator [J]. International Journal of Control, 1994, 60 (I): I17.
SUN Jie, ZHANG Dian-hua, LI Xu, ZHANG Jin, DU De-shun. Smith prediction monitor AGC system based on fuzzy self-tuning PID control [J]. Journal of Iron and Steel Research International, 2010, 17(2): 22–26.
HOU Zhong-sheng, XU Jian-Xin. A new feedback-feedforward configuration for the iterative learning control of a class of discrete-time system [J]. Acta Automatica Sinica, 2007, 33(3): 323–326.
SUN Ming-xuan, YAN Qiu-zhen. Design studies for error tracking of iterative learning control systems [J]. Acta Automatica Sinica, 2012, 38(11): 1–12. (in Chinese)
YANG Ming, GAO Yang, YU Yong, XU Dian-guo. On-line self-tuning of PI controller for PMSM drives based on the iterative learning control [J]. Electric Machines and Control, 2005, 9(6): 588–592. (in Chinese)
ZHANG Huai-xiang, YUAN Kui, ZOU Wei. Design of PID controller based on iterative learning control [J]. Systems Engineering and Electronics, 2006, 28(8): 1225–1228. (in Chinese)
LI Xun, SONG Dong-qiu, YU Shou-yi, GUI Wei-hua. Feedback automatic gauge control system using model reference adaptive Smith predictor [J]. Control Theory & Applications, 2009, 26(9): 999–1003. (in Chinese)
PALMOR Z. Stability properties of Smith dead time compensator controllers [J]. International Journal of Control, 1980, 32(7): 938–947.
ZHOU Han-Qin, WANG Qing-guo, MIN Liu. Modified Smith predictor design for periodic disturbance rejection [J]. ISA Transactions, 2007, 46: 493–503.
IBRAHIM KAYA. Obtaining controller parameters for a new PI-PD Smith predictor using autotuning [J]. Journal of Process Control, 2003, 13:465–472.
HU Qiu-ping, XU Jian-xin, LEE Tong-Hong. Iterative learning control design for Smith predictor [J]. Systems & Control Letters, 2001, 44: 201–210.
LIANG Li-hua. Hydraulic drive and electrhydraulic servo system [M]. Harbin: Harbin Engineering University Press. 2005. (in Chinese)
Author information
Authors and Affiliations
Corresponding author
Additional information
Foundation item: Project(51074051) supported by the National Natural Science Foundation of China
Rights and permissions
About this article
Cite this article
Zhang, Hy., Sun, J., Zhang, Dh. et al. Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control. J. Cent. South Univ. 21, 3492–3497 (2014). https://doi.org/10.1007/s11771-014-2327-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11771-014-2327-3