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Experimental investigation of control updating period monitoring in industrial PLC-based fast MPC: Application to the constrained control of a cryogenic refrigerator

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Abstract

In this paper, a complete industrial validation of a recently published scheme for on-line adaptation of the control updating period in model predictive control is proposed. The industrial process that serves in the validation is a cryogenic refrigerator that is used to cool the supra-conductors involved in particle accelerators or experimental nuclear reactors. Two recently predicted features are validated: the first states that it is sometimes better to use less efficient (per iteration) optimizer if the lack of efficiency is over-compensated by an increase in the updating control frequency. The second is that for a given solver, it is worth monitoring the control updating period based on the on-line measured behavior of the cost function.

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References

  1. D. Q. Mayne, J. B. Rawlings, C. V. Rao, et al. Constrained model predictive control: Stability and optimality. Automatica, 2000, 36(6): 789–814.

    Article  MathSciNet  MATH  Google Scholar 

  2. M. Alamir. Stabilization of Nonlinear Systems Using Receding- Horizon Control Schemes: A Parameterized Approach for Fast Systems. Berlin: Springer, 2006.

  3. M. Diehl, H. G. Bock, J. P. Schlöder. A real-time iteration scheme for nonlinear optimization in optimal feedback control. SIAM Journal on Control and Optimization, 2005, 43(5): 1714–1736.

    Article  MathSciNet  MATH  Google Scholar 

  4. V. M. Zavala, C. D. Laird, L. T. Biegler. Fast implementation and rigorous models: can both be accommodated in NMPC? International Journal of Robust and Nonlinear Control, 2008, 18(8): 800–815.

    Article  MathSciNet  MATH  Google Scholar 

  5. M. Diehl, R. Findeisen, F. Allgower, et al. Nominal stability of realtime iteration scheme for nonlinear model predictive control. IEE Proceedings–Control Theory and Applications, 2005, 152(3): 296–308.

    Article  Google Scholar 

  6. M. Alamir. A framework for monitoring control updating period in real-time NMPC schemes. Nonlinear Model Predictive Control. L. Magni, D. Raimondo, F. Allgöwer (eds.). Berlin: Springer,2009: 433–445.

    Chapter  Google Scholar 

  7. M. Alamir. Monitoring control updating period in fast gradient based NMPC. European Control Conference, Zurich: IEEE, 2013: 3621–3626.

    Google Scholar 

  8. M. Alamir. Fast NMPC, a reality-steered paradigm: Key properties of fast NMPC algorithms. European Control Conference, Strasbourg: IEEE, 2014: 2472–2477.

    Google Scholar 

  9. M. Alamir. From certification of algorithms to certified MPC: The missing links. IFAC-Papers Online, 2015, 48(23): 65–72.

    Article  Google Scholar 

  10. A. Bomporad. Simple and certifiable quadratic programming algorithms for embedded linear model predictive control. IFAC Proceedings Volumes, 2012, 45(17): 14–20.

    Article  Google Scholar 

  11. C. N. Jones, A. Domahidi, M. Morari, et al. Fast predictive control: Real-time computation and certification. IFAC Proceedings Volumes, 2012, 45(17): 94–98.

    Article  Google Scholar 

  12. Y. Nesterov. A method of solving a convex programming problem with convergence rate O(1/k2). Soviet Mathematics Doklady, 1983, 27(2): 372–376.

    MATH  Google Scholar 

  13. H. Ferreau, H. G. Bock, M. Diehl. An online active set strategy to overcome the limitations of explicit MPC. International Journal of Robust and Nonlinear Control, 2008, 18(8): 816–830.

    Article  MathSciNet  MATH  Google Scholar 

  14. H. Ferreau, C. Kirches, A. Potschka, et al. qpOASES: A parametric active-set algorithm for quadratic programming. Mathematical Programming Computation, 2014, 6(4): 327–363.

    Article  MathSciNet  MATH  Google Scholar 

  15. R. E. Bank, W. M. Fichtner, W. Fichtner, et al. Transient simulation of silicon devices and circuits. IEEE Transactions on Electron Devices, 1986, ED-32(10): 1992–2007.

    Google Scholar 

  16. F. Bonne, M. Alamir, P. Bonnay, et al. Model based multivariable controller for large scale compression stations. Design and experimental validation on the LHC 18kW cryorefrigerator. AIP Conference Proceedings, Anchorage: American Institute of Physics, 2014: DOI 10.1063/1.4860899.

    Google Scholar 

  17. F. Clavel, M. Alamir, P. Bonnay, et al. Multivariable control architecture for a cryogenic test facility under high pulsed loads: Model derivation, control design and experimental validation. Journal of Process Control, 2011, 21(7): 1030–1039.

    Article  Google Scholar 

  18. F. Clavel. Modélisation et Controle d’un Réfrigérateur Cryog énique. Application à la Station 800W à 4.5K du CEA Grenoble. Ph.D. thesis. Grenoble, France: University of Grenoble-Alpes, 2011.

  19. F. Bonne, M. Alamir, P. Bonnay. Nonlinear observers of the thermal loads applied to the helium bath of a cryogenic Joule-Thompson cycle. Journal of Process Control, 2014, 24(3): 73–80.

    Article  Google Scholar 

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Acknowledgments

Authors would like to thank every co-worker from the SBT for their kind help to improve models and control strategy and for their time to correct and discuss this paper. Authors give special thanks to Michel Bon-Mardion, Lionel Monteiro, François Millet, Christine Hoa, Bernard Rousset and Jean-Marc Poncet from SBT for their explanation about the process and their participation on experimental campaigns.

Author information

Authors and Affiliations

  1. Université Grenoble Alpes, INAC-SBT, F-3800, Grenoble, France

    François Bonne & Patrick Bonnay

  2. CNRS, Gipsa-lab, Université Grenoble Alpes, F-3800, Grenoble, France

    Mazen Alamir

Authors
  1. François Bonne
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  2. Mazen Alamir
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  3. Patrick Bonnay
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Corresponding author

Correspondence to Mazen Alamir.

Additional information

This work was supported by the French ANR Project CRYOGREEN.

François BONNE was born in France in 1987. He received the M.Sc. in Control Systems from the University of Bordeaux, France, in 2011. François Bonne received his Ph.D. in 2014 from the Universit´e Grenoble Alpes, studying real-time model predictive control (MPC) applied to large scale cryo-refrigeration systems. He is now working with CEA (French atomic and alternative energy commission) to optimize the design and the operation of large scale cryogenic systems.

Mazen ALAMIR graduated in Mechanics (Grenoble, 1990) and Aeronautics (Toulouse, 1992). He received his Ph.D. in Nonlinear Model Predictive Control in 1995. Since 1996, he has been a CNRS research associate in the Control Systems Department of the Gipsa-lab, Grenoble. His main research topics are model predictive control, receding horizon observers, nonlinear hybrid systems, signature-based diagnosis, optimal cancer treatment and industrial applications. He served as head of the “Nonlinear Systems and Complexity” research group in the Control Systems Department of the Gipsa-lab, Grenoble. Home page: http://www.mazenalamir.fr.

Patrick BONNAY was born in France in 1971. He received the Graduate engineer of “conservatoire des arts et m´ etiers” specialized in control systems in 2000. He’s been working for the CEA in the field of automation since 1994. He is currently the team leader of the Electronics and Control laboratory, and is responsible for the control systems of the cryogenic installations at the Low Temperature Service (SBT). During the past several years, he has been designing control system of cryogenics installations like the regulation of temperature of Laser Mega Joule target with a variation less than 1 Milli Kelvin and he has been actively involved in research projects which include model predictive control apply on cryogenics systems.

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Bonne, F., Alamir, M. & Bonnay, P. Experimental investigation of control updating period monitoring in industrial PLC-based fast MPC: Application to the constrained control of a cryogenic refrigerator. Control Theory Technol. 15, 92–108 (2017). https://doi.org/10.1007/s11768-017-6109-y

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  • DOI: https://doi.org/10.1007/s11768-017-6109-y

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