Metall. Res. Technol.
Volume 117, Number 3, 2020
|Number of page(s)||11|
|Published online||01 June 2020|
- Q.K. Pan, L. Gao, L. Wang, A multi-objective hot-rolling scheduling problem in the compact strip production, Appl. Math. Modell. 73(1), 327–348 (2019) [CrossRef] [Google Scholar]
- J. Pittner, M.A. Simaan, Advanced control to reduce the likelihood of cobbles in the tandem rolling of hot metal strip, IEEE Trans. Ind. Appl. 51(5), 4305–4312 (2015) [Google Scholar]
- D.H. Zhang, W. Peng, J.G. Ding, Automation system and its development for hot strip mill, Steel Roll 32(2), 6–12 (2015) [Google Scholar]
- L.J. Sun, C. Shao, L. Zhang, Dynamic tracking prediction control of exit strip thickness based on improved fractal, Metall. Res. Technol. 114(4), 1–14 (2015) [Google Scholar]
- L.J. Sun, C. Shao, L. Zhang, A strip thickness prediction method of hot rolling based on D_S information reconstruction, J. Cent. South Univ. 22(1), 2192–2200 (2015) [CrossRef] [Google Scholar]
- H.Y. Zhang, J. Sun, D.H. Zhang, Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control, J. Cent. South Univ. Technol. 21(9), 3492–3497 (2014) [CrossRef] [Google Scholar]
- X.R. Min, J.X. Zhou, A new method of the steel strip thickness control forecasting based on optimized RBF neutral network using IPSO algorithm, J. Inner Mong. Univ. Sci. Technol. 35(1), 75–79 (2016) [Google Scholar]
- H.N. Bu, Z.W. Yan, D.H. Zhang, Rolling-schedule multi-objective optimization based on influence function for thin-gauge steel strip in tandem cold rolling, Scientia Iranica 23(6), 2663–2672 (2016) [CrossRef] [Google Scholar]
- Y.F. Ji, D.H. Zhang, S.Z. Chen, Algorithm design and application of novel GM-AGC based on mill stretch characteristic curve, J. Cent. South Univ. Technol. 21(2), 942–947 (2014) [CrossRef] [Google Scholar]
- L.J. Sun, C. Shao, L. Zhang, Hybrid dynamic continuous strip thickness prediction of hot rolling, Eng. Lett. 25(3), 268–276 (2017) [Google Scholar]
- F.C. Yin, J. Sun, D.H. Zhang, Sliding mode variable structure control for smith prediction monitoring AGC system based on double power reaching law, J. Braz. Soc. Mech. Sci. Eng. 38(6), 1731–1743 (2016) [CrossRef] [Google Scholar]
- F.C. Yin, D.H. Zhang, X. Li, Iterative learning control with an improved internal model for a monitoring automatic-gauge-control system, Metallurgist 59(9–10), 987–997 (2016) [CrossRef] [Google Scholar]
- J. Sun, D.H. Zhang, X. Li, Smith prediction monitor AGC system based on fuzzy self-tuning PID control, J. Iron Steel Res. Int. 17(2), 22–26 (2010) [CrossRef] [Google Scholar]
- Y.J. Hu, J. Sun, S.Z. Chen, Optimal control of tension and thickness for tandem cold rolling process based on receding horizon control, Ironmak. Steelmak. 29(1), 1–10 (2019) [Google Scholar]
- R. Ammar, G. Said, Dynamic matrix control and generalized predictive control, comparison study with IMC-PID, Int. J. Hydrog. Energy 28(13), 17561–17570 (2017) [Google Scholar]
- L.M. Wang, Y. Cheng, J. Zou, Battery available power prediction of hybrid electric vehicle based on improved Dynamic Matrix Control algorithms, J. Power Sources 261(5), 337–347 (2014) [Google Scholar]
- D.M. Lima, J.E. Normey-Rico, T.M. Santos, Temperature control in a solar collector field using Filtered Dynamic Matrix Control, ISA Trans. 62(1), 39–49 (2015) [Google Scholar]
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