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Particle Swarm Optimization Based NMPC: An Application to District Heating Networks

  • Guillaume Sandou
  • Sorin Olaru
Part of the Lecture Notes in Control and Information Sciences book series (LNCIS, volume 384)

Abstract

Predictive control is concerned with the on-line solution of successive optimization problems. As systems are more and more complex, one of the limiting points in the application of optimal receding horizon strategy is the tractability of these optimization problems. Stochastic optimization methods appear as good candidates to overcome some of the difficulties. Indeed, these methods are not dependent on the structure of costs and constraints (linear, convex...), can escape from local minima and do not require the computation of local informations (gradient, hessian). In this paper, a Particle Swarm Optimization (PSO) is proposed to solve the receding horizon principle with an application to district heating networks. Tests of the approach are given for a network benchmark, showing that more than satisfactory results are achieved, compared with classical control laws for such systems.

Keywords

Particle swarm optimization (PSO) NMPC Energy savings 

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References

  1. 1.
    Sandou, G., Olaru, S.: Ant colony and genetic algorithm for constrained predictive control of power systems. In: Bemporad, A., Bicchi, A., Buttazzo, G. (eds.) HSCC 2007. LNCS, vol. 4416, pp. 501–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  2. 2.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  3. 3.
    Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Proceedings of the Seventh Annual Conference on Evolutionary Programming, pp. 591–600 (1998)Google Scholar
  4. 4.
    Eberhart, R.C., Shi, Y.: Comparing inertia weigthts and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (2000)Google Scholar
  5. 5.
    Kennedy, J., Clerc, M.: Standard PSO (2006), http://www.particleswarm.info/Standard-PSO-2006.c
  6. 6.
    Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), pp. 1931–1938 (1999)Google Scholar
  7. 7.
    Sandou, G., Font, S., Tebbani, S., Hiret, A., Mondon, C.: Global modelling and simulation of a district heating network. In: Proceedings of the 9th International Symposium on District Heating and Cooling (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Guillaume Sandou
    • 1
  • Sorin Olaru
    • 1
  1. 1.Automatic Control DepartmentSUPELECGif-sur-YvetteFrance

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