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Multi-layer Decentralized MPC of Large-scale Networked Systems

  • C. Ocampo-Martinez
  • V. Puig
  • J. M. Grosso
  • S. Montes-de-Oca
Chapter
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 69)

Abstract

In this chapter, a multi-layer decentralized model predictive control (ML-DMPC) approach is proposed and designed for its application to large-scale networked systems (LSNS). This approach is based on the periodic nature of the system disturbance and the availability of both static and dynamic models of the LSNS. Hence, the topology of the controller is structured in two layers. First, an upper layer is in charge of achieving the global objectives from a set \(\mathcal {O}\) of control objectives given for the LSNS. This layer works with a sampling time \(\Delta t _1\), corresponding to the disturbances period. Second, a lower layer, with a sampling time \(\Delta t _2\), \(\Delta t _1 > \Delta t _2\), is in charge of computing the references for the system actuators in order to satisfy the local objectives from the set of control objectives \(\mathcal {O}\). A system partitioning allows to establish a hierarchical flow of information between a set \(\mathcal {C}\) of controllers designed based on model predictive control (MPC). Therefore, the whole proposed ML-DMPC strategy results in a centralized optimization problem for considering the global control objectives, followed of a decentralized scheme for reaching the local control objectives. The proposed approach is applied to a real case study: the water transport network of Barcelona (Spain). Results obtained with selected simulation scenarios show the effectiveness of the proposed ML-DMPC strategy in terms of system modularity, reduced computational burden and, at the same time, the admissible loss of performance with respect to a centralized MPC (CMPC) strategy.

Keywords

Control Objective Model Predictive Control Storage Element Global Objective Flow Source 
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.

Notes

Acknowledgments

This work has been partially funded by the Spanish Ministry of Science and Technology through the WATMAN project (DPI2009-13744).

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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • C. Ocampo-Martinez
    • 1
  • V. Puig
    • 1
  • J. M. Grosso
    • 1
  • S. Montes-de-Oca
    • 2
  1. 1.Institut de Robòtica i Informàtica Industrial (CSIC-UPC)Technical University of CataloniaBarcelonaSpain
  2. 2.Automatic Control DepartmentTechnical University of CataloniaBarcelonaSpain

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