A Multiagent Approach Using Model-Based Predictive Control for an Irrigation Canal

  • Van Thang PhamEmail author
  • Clément Raïevsky
  • Jean-Paul Jamont
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 293)


This paper presents the application of the multiagent paradigm to a distributed model-based predictive control (DMPC) scheme in order to improve its fault tolerance, give it the ability to dynamically adapt its strategy to optimize energy consumption, and to allow it to scale up. This approach is illustrated in the control of a canal simulated using realistic, physics-based 1D models in MatLab. The individual agent behavior, based on DMPC, and the multiagent composition mechanism are described. Presented simulation results illustrate the ability of the proposed control scheme to adapt to a hardware failure and to take global strategies into account.


distributed control water system predictive control 


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  1. 1.
    Malaterre, P.O., Rogers, D.C., Schuurmans, J.: Classification of canal control algorithms. Journal of Irrigation and Drainage Engineering (1998)Google Scholar
  2. 2.
    Litrico, X., Fromion, V.: Modeling and Control of Hydrosystems - A Frequency Domain Approach. Springer-Verlag (2009)Google Scholar
  3. 3.
    Mayne, D.Q., Rawlings, J.B., Rao, C.V., Scokaert, P.O.M.: Constrained model predictive control: Stability and optimality. Automatica, 789–814 (2000)Google Scholar
  4. 4.
    Findeisen, R., Imsland, L., Allgöwer, F., Foss, B.A.: State and output feedback nonlinear model predictive control: An overview. Euro. J. of Control 9(3), 179–195 (2003)Google Scholar
  5. 5.
    Mayne, D.Q., Michalska, H.: Receding horizon control of nonlinear systems. IEEE Transactions on Automatic Control 35(7), 814–824 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  6. 6.
    Qin, S., Badgwell, T.: An overview of industrial model predictive control technology. In: Kantor, J., Garcia, C., Carnahan, B. (eds.) Fifth International Conference on Chemical Process Control - CPCV, pp. 232–256. American Institute of Chemical Engineers (1996)Google Scholar
  7. 7.
    Jamont, J.P., Occello, M., Lagréze, A.: A multiagent approach to manage communication in wireless instrumentation systems. Measurement 43(4), 489–503 (2010)CrossRefGoogle Scholar
  8. 8.
    Matei, A.M.: Multi-agent system for monitoring and analysis prahova hydrographical basin. Technical report (2011)Google Scholar
  9. 9.
    Luo, J., Xu, L., Jamont, J.P., Zeng, L., Shi, Z.: Flood decision support system on agent grid: method and implementation. Enterprise Information Sys. 1(1), 49–68 (2007)CrossRefGoogle Scholar
  10. 10.
    Rendón-Sallard, T., Sànchez-Marrè, M., Aulinas, M., Comas, J.: Designing a multi-agent system to simulate scenarios for decision-making in river basin systems. In: Proc. of the 9th Int. Conf. of the AI R&D, pp. 291–298. IOS Press (2006)Google Scholar
  11. 11.
    Nabais, J.L., Mendonça, L.F., Botto, M.A.: A multi-agent architecture for diagnosing simultaneous faults along water canals. In: Control Engineering Practice (2013)Google Scholar
  12. 12.
    van Oel, P.R., Krol, M.S., Hoekstra, A.Y., Taddei, R.R.: Feedback mechanisms between water availability and water use in a semi-arid river basin: A spatially explicit mas simulation approach. Environmental Modelling & Software 25(4), 433–443 (2010)CrossRefGoogle Scholar
  13. 13.
    Occello, M., et al.: Designing organized agents for cooperation with real time constaints. In: Drogoul, A., Fukuda, T., Tambe, M. (eds.) CRW 1998. LNCS, vol. 1456, pp. 25–37. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Cohen, G., Zhu, D.L.: Decomposition coordination methods in large scale optimization problems: The nondifferentiable case and the use of augmented lagrangians. Advances in Large Scale Systems 1, 203–266 (1984)MathSciNetGoogle Scholar
  15. 15.
    Cohen, G.: Auxiliary problem principle and decomposition of optimization problems. Journal of Optimization Theory and Application 32(3) (1980)Google Scholar
  16. 16.
    Pham, V.T., Georges, D., Besançon, G.: Infinite-dimensional predictive control for hyperbolic systems. SIAM J. of Ctrl. and Optimisation (2012)Google Scholar
  17. 17.
    Scattolini, R.: Architectures for distributed and hierachical model predictive control. Journal for Process Control, 723–731 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Van Thang Pham
    • 1
    Email author
  • Clément Raïevsky
    • 1
  • Jean-Paul Jamont
    • 1
  1. 1.Laboratoire LCISUniversité de Grenoble AlpesValenceFrance

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