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)


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.


Particle swarm optimization (PSO) NMPC Energy savings 


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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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