Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics**This work is supported by the ANR project entitled Hamiltonian Methods for the Control of Multidomain Distributed Parameter Systems, HAMECMOPSYS financed by the French National Research Agency. Further information is available at http://www.hamecmopsys.ens2m.fr/.-Reference-Cited by-同舟云学术

Making Non-Centralized a Model Predictive Control Scheme by Using Distributed Smith Dynamics**This work is supported by the ANR project entitled Hamiltonian Methods for the Control of Multidomain Distributed Parameter Systems, HAMECMOPSYS financed by the French National Research Agency. Further information is available at http://www.hamecmopsys.ens2m.fr/.

Published:2015 Issue:23 Volume:48 Page:501-506
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Barreiro-Gomez J.,Obando G.,Ocampo-Martinez C.,Quijano N.

Publisher

Elsevier BV

Subject

Control and Systems Engineering

Reference13 articles.

1. Barreiro-Gomez, J., Obando, G., and Quijano, N. (2014). Distributed population dynamics: Optimization and control applications. IEEE Transactions on Systems, Man, and Cybernetics (Submitted).

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3. Computational Aspects of Distributed Optimization in Model Predictive Control.;Conte;In Proceedings of the 51st IEEE Conference on Decision and Control (CDC),2012

4. A survey on distributed estimation and control applications using linear consensus algorithms.;Garin;Networked Control Systems,2010

5. Game theory and distributed control. In Handbook of Game Theory, volume 4;Marden,2015

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