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Distributed Dual-rate Consensus Predictive Control of Looper Tension System in Hot Rolling Mills

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  • Control Theory and Applications
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Abstract

This paper considers a dual-rate distributed predictive control strategy for the looper tension system in hot rolling mills, which is a typical multi-agent system with directed communication topology. First, we establish an interconnected model for looper tension control system and the disturbances from the neighbors are considered effectively. Second, the consensus control protocol is developed based on the proposed control strategy to improve the robustness and stability of the multi-agent, and the sufficient conditions for consensus are developed. We update and implement all the agent controllers sequentially in one output sampling period and begin a new cycle at the next sampling instant, which leads the multi-agent control system is of fast control updating rate and slow output sampling rate. The control inputs of neighbors can be obtained to compensate the coupling effects, and the cooperation of controllers are improved. Finally, simulation results verify the proposed control strategy and corresponding results.

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References

  1. X. Yu and L. Liu, “Distributed circular formation control of ring-networked nonholonomic vehicles,” Automatica, vol. 68, pp. 92–99, February 2016. [click]

    Article  MathSciNet  MATH  Google Scholar 

  2. C. Wang and C. J. Ong, “Distributed model predictive control of dynamically decoupled systems with coupled cost,” Automatica, vol. 46, no. 5, pp. 2053–2058, October 2010. [click]

    Article  MathSciNet  MATH  Google Scholar 

  3. H.-P. Li, Y. Shi, and W.-S. Yan, “Distributed receding horizon control of constrained nonlinear vehicle formations with guaranteed g -gain stability,” Automatica, vol. 68, pp. 148–154, February 2016. [click]

    Article  MathSciNet  MATH  Google Scholar 

  4. H. T. Zhang, C. Zhai, and Z. Y. Chen, “A general alignment repulsion algorithm for flocking of multi-agent systems,” IEEE Transactions on Automatic Control, vol. 56, no. 2 pp. 430–435, February 2011. [click]

    Article  MathSciNet  MATH  Google Scholar 

  5. C.-R Wang and H.-B. Ji, “Leader-following consensus of multi-agent systems under directed communication topology via distributed adaptive nonlinear protocol,” Systems & Control Letters, vol. 70, pp. 23–29, June 2014. [click]

    Article  MathSciNet  MATH  Google Scholar 

  6. J. L. Wang and H. N. Wu, “Leader-following formation control of multi agent systems under fixed and switching topologies,” Int. J. Control, vol. 85, no. 6 pp. 695–705, 2012. [click]

    Article  MathSciNet  MATH  Google Scholar 

  7. Y. Cao and W. Ren, “Multi-vehicle coordination for double integrator dynamics under fixed undirected/directed interaction in a sampled data setting,” International Journal of Robust and Nonlinear Control, vol. 20, pp. 987–999, 2010. [click]

    MathSciNet  MATH  Google Scholar 

  8. L. Wang and F. Xiao, “Finite-time consensus problems for networks of dynamic agents,” IEEE Transactions on Automatic Control, vol. 55, no. 4 pp. 950–955, 2010. [click]

    Article  MathSciNet  MATH  Google Scholar 

  9. S. Li, H. Du, and X. Lin, “Finite-time consensus algorithm for multi-agent systems with double-integrator dynamics,” Automatica, vol. 47, pp. 1706–1712, 2011.[click]

    Article  MathSciNet  MATH  Google Scholar 

  10. J.-S. Huang, C.-Y. Wen, W. Wang, and Y.-D. Song, “Adaptive finite-time consensus control of a group of uncertain nonlinear mechanical systems,” Automatica, vol. 51, pp. 292–301, 2015. [click]

    Article  MathSciNet  MATH  Google Scholar 

  11. H. Zhang, F. Lewis, and A. Das, “Optimal design for synchronization of cooperative systems: state feedback, observer and output feedback,” IEEE Transactions on Automatic Control, vol. 56, no. 8, pp. 1948–1952, 2011. [click]

    Article  MathSciNet  MATH  Google Scholar 

  12. J.-T Li, Y. Wang, and H.-M. Xiao, “Consensus Seeking of Multi-agent Systems from an Iterative Learning Perspective,” International Journal of Control, Automation and Systems, vol. 14, no. 5, pp. 1173–1182, 2016. [click]

    Article  MathSciNet  Google Scholar 

  13. I. D. Couzin, J. Krause, R. James, G. D. Ruxton, and N. R. Franks, “Collective memory and spatial sorting in animal groups,” J. Theoretical Biol., vol. 218, pp. 1–11, 2012. [click]

    Article  MathSciNet  Google Scholar 

  14. P. D. Christofides, R. Scattolini, D.-M. Pena, and J.-F Liu, “Distributed model predictive control: A tutorial review and future research directions,” Computers and Chemical engineering, vol. 51, pp. 21–41, 2013. [click]

    Article  Google Scholar 

  15. Z.-M. Cheng, H.-T Zhang, M.-C Fan, and G.-R. Chen, “Distributed consensus of multi-agent systems with input constraints: A model predictive control approach,” IEEE transactions on circuits and systems-1:regular papers, vol. 62, no. 3, pp. 825–834, 2015. [click]

    Article  MathSciNet  Google Scholar 

  16. G. Ferrari-Trecate, L. Galbusera, M. P. E. Marciandi, and R. Scattolini, “Model predictive control schemes for consensus in multi-agent systems with single- and doubleintegrator dynamics,” IEEE Trans.Autom. Control, vol. 54, no. 11, pp. 2560–2572, 2009. [click]

    Article  MATH  Google Scholar 

  17. E. Camponogara and H. F. Scherer, “Distributed optimization for model predictive control of linear dynamic networks with control-input and output constraints,” IEEE Transactions on Automation Science and Engineering, vol. 8, no. 1, pp. 233–242, 2011. [click]

    Article  Google Scholar 

  18. A. Richards and J. P. How, “Robust distributed model predictive control,” International Journal of Control, vol. 80, no.9, pp. 1517–1531, 2007. [click]

    Google Scholar 

  19. H.-T. Zhang, M. Z. Q. Chen, and G.-B. Stan, “Fast consensus via predictive pinning control, IEEE Trans,” Automatica, vol. 58, no. 9, pp. 2247–2258, 2011. [click]

    Google Scholar 

  20. Y. Yuan, G. B. Stan, L. Shi, M. Barahona, and J. Goncalves, “Decentralized minimum-time consensus,” Automatica, vol. 49, pp. 1227–1235, 2013. [click]

    Article  MATH  Google Scholar 

  21. E. Camponogara and L. B. de Oliveira, “Distributed optimization for model predictive control of linear-dynamic networks,” IEEE Trans. Syst.,Man Cybern. A, vol. 39, no. 6, pp. 1331–1338, 2009. [click]

    Article  Google Scholar 

  22. K.-V. Ling, W.-K. Ho, and B.-F. Wu, “Multiplexed MPC for multizone thermal processing in semiconductor manufacturing,” IEEE transactions on control system technology, vol. 18, no. 6, pp. 1371–1380, 2010. [click]

    Article  Google Scholar 

  23. K.-V. Ling, W.-K. Ho, Y. Feng, and B.-F Wu, “Integralsquare- error performance of multiplexed model predictive control,” IEEE transactions on industrial informatics, vol. 7, no. 2, pp. 196–203, 2011. [click]

    Article  Google Scholar 

  24. P. Wang and B. Ding, “Distributed RHC for tracking and formation of nonholonomic multi-vehicle systems,” IEEE transactions on automatic control, vol. 59, no. 6, pp. 1439–1453, 2014.[click]

    Article  MathSciNet  MATH  Google Scholar 

  25. H. M. Kristian and F. L. Lewis, “Cooperative optimal control for multi-agent systems on directed graph topologies,” IEEE Transactions on Automatic Control, vol. 59, no. 3, pp. 769–774, 2014. [click]

    Article  MathSciNet  MATH  Google Scholar 

  26. H. M. Kristian, F. L. Lewis, and S. Michael, “Distributed static output-feedback control for state synchronization in networks of identical LTI systems,” Automatica, vol. 53, pp. 282–290, 2015. [click]

    Article  MathSciNet  MATH  Google Scholar 

  27. H.-P. Li and W.-S. Yan, “Receding horizon control based on consensus scheme in general linear multi-agent systems,” Automatica, vol. 56, pp. 12–18, 2015. [click]

    Article  MathSciNet  MATH  Google Scholar 

  28. I. S. Choi, J. A. Rossiter, and P. J. Fleming, “Looper and tension control in hot rolling mills: A survey,” Journal of Process Control, vol. 17, pp. 509–521, 2007. [click]

    Article  Google Scholar 

  29. S. Ye, “Decoupling control and simulation of looper MIMO system for hot strip rolling mill,” Advanced Materials Research, vol.846, pp. 360–364, 2014. [click]

    Google Scholar 

  30. H.-W. Wang, Y.-W. Jing, and C. Yu, “Guaranteed cost sliding mode control for looper-tension multivariable uncertain systems,” Nonlinear Dynamics, vol. 80, no. 1 pp. 39–50, 2015.

    Article  MathSciNet  MATH  Google Scholar 

  31. C.-N. Tong, Y.-K. Wu, L.-M. Liu, and J.-Y. Li, “Modeling and integral variable structure control of hydraulic looper multivariable system,” Acat Automatic Sinica, vol. 34, no. 10 pp. 1305–1311, 2008.

    Article  MathSciNet  Google Scholar 

  32. C. J. Park and I. C. Hwang, “Tension control in hot strip process using adaptive receding horizon control,” Journal of Materials Processing Technology, vol. 209, pp. 426–434, 2009. [click]

    Article  Google Scholar 

  33. M. Heidarinejad, J. Liu, D. M. de la Peña, J. F. Davis, and P. D. Christofides, “Multirate Lyapunov-based distributed model predictive control of nonlinear uncertain systems,” Journal of Process Control, vol. 21, pp. 1231–1242, 2011. [click]

    Article  Google Scholar 

  34. Q.-M. Shao and A. Cinara, “System identification and distributed control for multi-rate sampled systems,” Journal of Process Control, vol. 34, pp. 1–12, 2015. [click]

    Article  Google Scholar 

  35. D. Li, “Identification of fast-rate models from multirate data,” International Journal of Control, vol. 74, pp. 680–689, 2001. [click]

    Article  MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer and Communication Engineering, China University of Petroleum, Qingdao, 2666580, China

    Xiao-Dong Zhang, Shao-Shu Gao, Xin-Ping Liu & Ting-Pei Huang

Authors
  1. Xiao-Dong Zhang
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  2. Shao-Shu Gao
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  3. Xin-Ping Liu
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  4. Ting-Pei Huang
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Corresponding author

Correspondence to Shao-Shu Gao.

Additional information

Recommended by Associate Editor Do Wan Kim under the direction of Editor Yoshito Ohta. This work was supported by the Shandong Provincial Natural Science Foundation, China (grant ZR2014FP013); Qingdao Research Foundation for Basic Research China(grant 14-2-4-115 jch); The Economy & Technology Development of Zone of Qingdao Development of Science and Technology Plan Major Projects (grant 2013-1-37) and the Fundamental Research Funds for the Central Universities (grant 16CX02008A, 16CX02007A). National Natural Science Foundation of China (NSFC)(Grant 61402433).

Xiao-Dong Zhang received his B.S. degree in Automation from QiQiHar University of China in 2002 and received an M.S. degree in Control Theory and Control Engineering from Herbin University of Science and Technology, China, in 2006 and received a Ph.D. degree in control theory and control engineering at the Beijing Institute of Technology, China in 2011. Currently, he is a teacher in the College of Computer and Communication Engineering, China University of Petroleum, Shandong, China. His research interests include predictive control, robust control and data driven control.

Shao-Shu Gao received the B.S. degree in Electrical Engineering from North University of China in 2007 and received her Ph.D. degree in optical engineering at the Beijing Institute of Technology, China in 2013. Currently, she is a teacher in the College of Computer and Communication Engineering, China University of Petroleum, Shandong, China. Her research interests include image processing and color night.

Xin-Ping Liu received the B.S. degree in Mechanical and Eletric from China University of Petroleum in 1988, and his Ph.D. degree in 2009. Currently, he is a an Associate Professor in the College of Computer and Communication Engineering, China University of Petroleum, Shandong, China. His research interests include control theory and its application.

Ting-Pei Huang received the B.E. and M.S. degrees from the College of Computer and Communication Engineering, China University of Petroleum, Shandong, China, in 2004 and 2007, respectively. She was a teacher in the Department of Computer Science and Technology at the Chuzhou University, Anhui, China, from 2007 to 2009. She obtained her Ph.D. degree in the Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China, in 2013. Currently, she is a teacher in the College of Computer and Communication Engineering, China University of Petroleum, Shandong, China. Her research interests include protocol design and performance evaluation for wireless sensor networks, internet of things and mobile networks.

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Zhang, XD., Gao, SS., Liu, XP. et al. Distributed Dual-rate Consensus Predictive Control of Looper Tension System in Hot Rolling Mills. Int. J. Control Autom. Syst. 16, 577–585 (2018). https://doi.org/10.1007/s12555-017-0091-5

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  • DOI: https://doi.org/10.1007/s12555-017-0091-5

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