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

  • Xiao-Dong Zhang
  • Shao-Shu Gao
  • Xin-Ping Liu
  • Ting-Pei Huang
Regular Paper Control Theory and Applications


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.


Consensus control distributed model predictive control dual-rate looper system 


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  1. [1]
    X. Yu and L. Liu, “Distributed circular formation control of ring-networked nonholonomic vehicles,” Automatica, vol. 68, pp. 92–99, February 2016. [click]MathSciNetCrossRefzbMATHGoogle Scholar
  2. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  3. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  4. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  5. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  6. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  7. [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]MathSciNetzbMATHGoogle Scholar
  8. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  9. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  10. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  11. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  12. [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]MathSciNetCrossRefGoogle Scholar
  13. [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]MathSciNetCrossRefGoogle Scholar
  14. [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]CrossRefGoogle Scholar
  15. [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]MathSciNetCrossRefGoogle Scholar
  16. [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]CrossRefzbMATHGoogle Scholar
  17. [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]CrossRefGoogle Scholar
  18. [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. [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. [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]CrossRefzbMATHGoogle Scholar
  21. [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]CrossRefGoogle Scholar
  22. [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]CrossRefGoogle Scholar
  23. [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]CrossRefGoogle Scholar
  24. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  25. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  26. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  27. [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]MathSciNetCrossRefzbMATHGoogle Scholar
  28. [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]CrossRefGoogle Scholar
  29. [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. [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.MathSciNetCrossRefzbMATHGoogle Scholar
  31. [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.MathSciNetCrossRefGoogle Scholar
  32. [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]CrossRefGoogle Scholar
  33. [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]CrossRefGoogle Scholar
  34. [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]CrossRefGoogle Scholar
  35. [35]
    D. Li, “Identification of fast-rate models from multirate data,” International Journal of Control, vol. 74, pp. 680–689, 2001. [click]MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Xiao-Dong Zhang
    • 1
  • Shao-Shu Gao
    • 1
  • Xin-Ping Liu
    • 1
  • Ting-Pei Huang
    • 1
  1. 1.College of Computer and Communication EngineeringChina University of PetroleumQingdaoChina

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