Dynamic matrix predictive control for a hydraulic looper system in hot strip mills

Fang-chen Yin , Jie Sun , Wen Peng , Hong-yu Wang , Jing Yang , Dian-hua Zhang

Journal of Central South University ›› 2017, Vol. 24 ›› Issue (6) : 1369 -1378.

PDF
Journal of Central South University ›› 2017, Vol. 24 ›› Issue (6) : 1369 -1378. DOI: 10.1007/s11771-017-3541-6
Article

Dynamic matrix predictive control for a hydraulic looper system in hot strip mills

Author information +
History +
PDF

Abstract

Controlling the looper height and strip tension is important in hot strip mills because these variables affect both the strip quality and strip threading. Many researchers have proposed and applied a variety of control schemes for this problem, but the increasingly strict market demand for strip quality requires further improvements. This work describes a dynamic matrix predictive control (DMC) strategy that realizes the optimal control of a hydraulic looper multivariable system. Simulation experiments for a traditional controller and the proposed DMC controller were conducted using MATLAB/Simulink software. The simulation results show that both controllers acquire good control effects with model matching. However, when the model is mismatched, the traditional controller produces an overshoot of 32.4% and a rising time of up to 2120.2 ms, which is unacceptable in a hydraulic looper system. The DMC controller restricts the overshoot to less than 0.08%, and the rising time is less than 48.6 ms in all cases.

Keywords

hot strip mill / hydraulic looper system / mathematical model / dynamic matrix predictive control

Cite this article

Download citation ▾
Fang-chen Yin, Jie Sun, Wen Peng, Hong-yu Wang, Jing Yang, Dian-hua Zhang. Dynamic matrix predictive control for a hydraulic looper system in hot strip mills. Journal of Central South University, 2017, 24(6): 1369-1378 DOI:10.1007/s11771-017-3541-6

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

SansalK Y, HuangB, ForbesJ F. Dynamics and variance control of hot mill loopers [J]. Control Engineering Practice, 2008, 16(1): 89-100

[2]

ChoiI S, RossiterJ A, FlemingP J. Looper and tension control in hot rolling mills: A survey [J]. Journal of Process Control, 2007, 17(6): 509-521

[3]

KazuyaA, KazuiroY, TakashiK, NobuakiN. Hot strip mill tension-looper control based on decentralization and coordination [J]. Control Engineering Practice, 2000, 8(3): 337-344

[4]

LiB-q, FuJ, ZhangR-c, SunY-kang. The decoupling control for the loopers’ height and tension system in hot strip finishing mill [J]. Journal of University of Science and Technology Beijing, 2007, 27(5): 596-599

[5]

LiB-q, ZhangK-j, FuJ, SunY-kang. Adaptive neural network decoupling control for the loopers’ height and tension system [J]. Control and Decision, 2006, 21(1): 46-50

[6]

RiccardoF, FrancescoA C, ThomasP. Friction compensation in the interstand looper of hot strip mills: a slidingmode control approach [J]. Control Engineering Practice, 2008, 16(2): 214-224

[7]

TaoG-l, LiuChao. Accurate calculation of loop tension moment of hot strip mill [J]. Steel Rolling, 2014, 31(3): 20-22

[8]

SharifiF J. A neuro-fuzzy system for looper tension control in rolling mills [J]. Control Engineering Practice, 2005, 13(1): 1-13

[9]

TongC-n, WuY-k, LiuL-m, LiJ-yun. Modeling and integral variable structure control of hydraulic looper multivariable system [J]. Acta Automatica Sinica, 2008, 34(10): 1305-1312

[10]

TimothyH, YuA J, DavidJ C, DavidH B. Controller design for hot strip finishing mills [J]. IEEE Transactions on Control Systems Technology, 1998, 6(2): 208-219

[11]

SchuurmansJ, JonesT. Control of mass flow in a hot strip mill using model predictive control [C]//. Proceedings of the 2002 IEEE International Conference on Control Applications, 2002379384

[12]

SunJ, ChenS-z, HanH-h, ChenX-h, ChenQ-j, ZhangD-hua. Identification and optimization for hydraulic roll gap control in strip rolling mill [J]. Journal of Central South University, 2015, 22(6): 2183-2191

[13]

ChenC-t, RennJ C, YanZ-yuan. Experimental identification of inertial and friction parameters for electro-hydraulic motion simulators [J]. Mechatronics, 2011, 21(1): 1-10

[14]

WangY, ZhangZ, QinX-qing. Modeling and control for hydraulic transmission of unmanned ground vehicle [J]. Journal of Central South University, 2014, 21(1): 124-129

[15]

JohnP, MarwanA S. Improvement in control of the tandem hot Strip Mill [J]. IEEE Transactions on Industrial Applications, 2013, 49(5): 1962-1970

[16]

JeongJ C, WanK H, JongS K. A self-tuning PI control system design for the flatness of hot strip in finishing mill processes [J]. KSME International Journal, 2004, 18(3): 379-387

[17]

PedroR, EfstratiosN P. A dynamic programming based approach for explicit model predictive control of hybrid systems [J]. Computers and Chemical Engineering, 2015, 72: 126-144

[18]

CamachoE F, RamirezD R, LimonD, MunozP, AlamoT. Model predictive control techniques for hybrid systems [J]. Annual Reviews in Control, 2010, 34(2): 21-31

[19]

SuB-l, ChenZ-q, YuanZ-zhi. Multivariable decoupling predictive control with input constraints and its application on chemical process [J]. Chinese Journal of Chemical Engineering, 2006, 14(2): 216-221

AI Summary AI Mindmap
PDF

104

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/