Intelligence in Transportation Infrastructures via Model-Based Predictive Control

  • R. R. NegenbornEmail author
  • H. Hellendoorn
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 42)


In this chapter we discuss similarities and differences between transportation infrastructures like power, road traffic, and water infrastructures, and present such infrastructures in a generic framework. We discuss from a generic point of view what type of control structures can be used to control such generic infrastructures, and explain what in particular makes intelligent infrastructures intelligent. We hereby especially focus on the conceptual ideas of model predictive control, both in centralized, single-agent control structures, and in distributed, multi-agent control structures. The need for more intelligence in infrastructures is then illustrated for three types of infrastructures: power, road, and water infrastructures.


Control Agent Control Structure Multiagent System Model Predictive Control Transportation Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    L. Acar. Some examples for the decentralized receding horizon control. In Proceedings of the 31st IEEE Conference on Decision and Control, pages 1356–1359, Tucson, Arizona, 1992.Google Scholar
  2. 2.
    M. Aicardi, G. Casalino, R. Minciardi, and R. Zoppoli. On the existence of stationary optimal receding-horizon strategies for dynamic teams with common past information structures. IEEE Transactions on Automatic Control, 37:1767–1771, November 1992.CrossRefGoogle Scholar
  3. 3.
    M. Arnold, R. R. Negenborn, G. Andersson, and B. De Schutter. Model-based predictive control applied to multi-carrier systems. In Proceedings of the IEEE PES General Meeting 2009, Calgary, Canada, June 2009.Google Scholar
  4. 4.
    K. J. Åström and B. Wittenmark. Computer-Controlled Systems. Prentice-Hall, Upper Saddle River, New Jersey, 1997.Google Scholar
  5. 5.
    M. Baglietto, T. Parisini, and R. Zoppoli. Neural approximators and team theory for dynamic routing: A receding-horizon approach. In Proceedings of the 38th IEEE Conference on Decision and Control, pages 3283–3288, Phoenix, Arizona, 1999.Google Scholar
  6. 6.
    D. P. Bertsekas. Nonlinear Programming. Athena Scientific, Beltmore, Massachusetts, 2003.Google Scholar
  7. 7.
    P. R. Bhave and R. Gupta. Analysis of Water Distribution Networks. Alpha Science International, Oxford, UK, 2006.Google Scholar
  8. 8.
    L. G. Bleris, P. D. Vouzis, J. G. Garcia, M. G. Arnold, and M. V. Kothare. Pathways for optimization-based drug delivery. Control Engineering Practice, 15(10):1280–1291, October 2007.CrossRefGoogle Scholar
  9. 9.
    M. W. Braun, D. E. Rivera, M. E. Flores, W. M. Carlyle, and K. G. Kempf. A model predictive control framework for robust management of multi-product, multi-echelon demand networks. Annual Reviews in Control, 27:229–245, 2003.CrossRefGoogle Scholar
  10. 10.
    E. F. Camacho and C. Bordons. Model Predictive Control in the Process Industry. Springer-Verlag, Berlin, Germany, 1995.Google Scholar
  11. 11.
    E. Camponogara, D. Jia, B. H. Krogh, and S. Talukdar. Distributed model predictive control. IEEE Control Systems Magazine, 1:44–52, February 2002.Google Scholar
  12. 12.
    C. F. Daganzo. Fundamentals of Transportation and Traffic Operations. Pergamon Press, New York, New York, 1997.Google Scholar
  13. 13.
    B. De Schutter, T. van den Boom, and A. Hegyi. A model predictive control approach for recovery from delays in railway systems. Transportation Research Record, (1793):15–20, 2002.Google Scholar
  14. 14.
    W. B. Dunbar and R. M. Murray. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 42(4):549–558, April 2006.CrossRefGoogle Scholar
  15. 15.
    H. El Fawal, D. Georges, and G. Bornard. Optimal control of complex irrigation systems via decomposition-coordination and the use of augmented Lagrangian. In Proceedings of the 1998 International Conference on Systems, Man, and Cybernetics, pages 3874–3879, San Diego, California, 1998.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Delft University of Technology, Delft Center for Systems and ControlDelftThe Netherlands

Personalised recommendations