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Mathematical Problems in Engineering
Volume 2016, Article ID 7454805, 16 pages
http://dx.doi.org/10.1155/2016/7454805
Research Article

Intelligent Model Building and GPC-PID Based Temperature Curve Control Strategy for Metallurgical Industry

1Department of Automation, Shanghai Jiao Tong University, 800 Dong Chuan Road, Shanghai 200240, China
2School of Automation, Huazhong University of Science and Technology, 1024 Luo Yu Road, Wuhan 430074, China

Received 26 November 2015; Accepted 9 February 2016

Academic Editor: Hiroyuki Mino

Copyright © 2016 Shuanghong Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Sikdar and A. Mukhopadhyay, “Numerical determination of heat transfer coefficient for boiling phenomenon at runout table of hot strip mill,” Ironmaking and Steelmaking, vol. 31, no. 6, pp. 495–502, 2004. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Serajzadeh, “Prediction of temperature distribution and phase transformation on the run-out table in the process of hot strip rolling,” Applied Mathematical Modelling, vol. 27, no. 11, pp. 861–875, 2003. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Zheng, S. Li, and X. Wang, “An approach to model building for accelerated cooling process using instance-based learning,” Expert Systems with Applications, vol. 37, no. 7, pp. 5364–5371, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. H.-B. Xie, X.-H. Liu, G.-D. Wang, and Z.-P. Zhang, “Optimization and model of laminar cooling control system for hot strip mills,” Journal of Iron and Steel Research International, vol. 13, no. 1, pp. 18–22, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. S. P. Guan, H.-X. Li, and S. K. Tso, “Multivariable fuzzy supervisory control for the laminar cooling process of hot rolled slab,” IEEE Transactions on Control Systems Technology, vol. 9, no. 2, pp. 348–356, 2001. View at Publisher · View at Google Scholar · View at Scopus
  6. G. van Ditzhuijzen, “The controlled cooling of hot rolled strip: a combination of physical modeling, control problems and practical adaption,” IEEE Transactions on Automatic Control, vol. 38, no. 7, pp. 1060–1065, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. D. M. Lee and S. G. Choi, “Application of on-line adaptable neural network for the rolling force set-up of a plate mill,” Engineering Applications of Artificial Intelligence, vol. 17, no. 5, pp. 557–565, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. H.-X. Li and S. P. Guan, “Hybrid intelligent control strategy: supervising a DCS-controlled batch process,” IEEE Control Systems Magazine, vol. 21, no. 3, pp. 36–46, 2001. View at Publisher · View at Google Scholar · View at Scopus
  9. E.-Y. Liu, D.-H. Zhang, J. Sun, L.-G. Peng, B.-H. Gao, and L.-T. Su, “Algorithm design and application of laminar cooling feedback control in hot strip mill,” Journal of Iron and Steel Research, International, vol. 19, no. 4, pp. 39–42, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Zheng, S. Y. Li, and X. Wang, “An approach to model building for accelerated cooling process using instance-based learning,” Expert Systems with Applications, vol. 37, no. 7, pp. 5364–5371, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. J. Wang, G.-D. Wang, and X.-H. Liu, “Hot strip laminar cooling control model,” Journal of Iron and Steel Research International, vol. 11, no. 5, pp. 13–17, 2004. View at Google Scholar · View at Scopus
  12. B. Han, Z. P. Zhang, X. H. Liu, and G. D. Wang, “Element tracking strategies for hot striplaminar cooling control,” Journal of Iron and Steel Research International, vol. 12, no. 3, pp. 18–21, 27, 2005. View at Google Scholar
  13. H. B. Xie, Z. Y. Jiang, X. H. Liu et al., “Application of fuzzy control of laminar cooling for hot rolled strip,” Journal of Materials Processing Technology, vol. 187-188, pp. 715–719, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Y. Gong, J. Z. Xu, L. G. Peng, G. D. Wang, and X. H. Liu, “Self-learning and its application to laminar cooling, model of hot rolled strip,” Journal of Iron and Steel Research International, vol. 14, no. 4, pp. 11–14, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. H. N. Han, J. K. Lee, H. J. Kim, and Y.-S. Jin, “A model for deformation, temperature and phase transformation behavior of steels on run-out table in hot strip mill,” Journal of Materials Processing Technology, vol. 128, no. 1–3, pp. 216–225, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. Y. Zheng, N. Li, and S. Y. Li, “Hot-rolled strip laminar cooling process plant-wide temperature monitoring and control,” Control Engineering Practice, vol. 21, no. 1, pp. 23–30, 2013. View at Publisher · View at Google Scholar · View at Scopus
  17. H.-X. Li and S. Guan, “Hybrid intelligent control strategy. Supervising a DCS-controlled batch process,” IEEE Control Systems, vol. 21, no. 3, pp. 36–48, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Tan, S. Li, J. Pian, and T. Chai, “Case-based modeling of the laminar cooling process in a hot rolling mill,” in Intelligent Control and Automation, D.-S. Huang, K. Li, and G. W. Irwin, Eds., vol. 344 of Lecture Notes in Control and Information Sciences, pp. 264–274, Springer, 2006. View at Publisher · View at Google Scholar
  19. Y. Zheng, S. Li, and X. Wang, “Distributed model predictive control for plant-wide hot-rolled strip laminar cooling process,” Journal of Process Control, vol. 19, no. 9, pp. 1427–1437, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985. View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  21. P. P. Angelov and D. P. Filev, “An approach to online identification of Takagi-Sugeno fuzzy models,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 1, pp. 484–498, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. H. J. Lee and D. W. Kim, “Robust stabilization of T-S fuzzy systems: fuzzy static output feedback under parametric uncertainty,” International Journal of Control, Automation and Systems, vol. 7, no. 5, pp. 731–736, 2009. View at Publisher · View at Google Scholar · View at Scopus
  23. D. W. Clarke, C. Mohtadi, and P. S. Tuffs, “Generalized predictive control. Part I. The basic algorithm,” Automatica, vol. 23, no. 2, pp. 137–148, 1987. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Sato, “Design of a GPC-based PID controller for controlling a weigh feeder,” Control Engineering Practice, vol. 18, no. 2, pp. 105–113, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. T. Sato, A. Inoue, and T. Yamamoto, “Improvement of tracking performance in designing a GPC based PID controller using a time-varying proportional gain,” IEEJ Transactions on Electrical and Electronic Engineering, vol. 1, no. 4, pp. 438–441, 2006. View at Publisher · View at Google Scholar