Distributed Optimization for MPC of Linear Dynamic Networks

  • E. CamponogaraEmail author
Part of the Intelligent Systems, Control and Automation: Science and Engineering book series (ISCA, volume 69)


This chapter presents existing models and distributed optimization algorithms for model predictive control (MPC) of linear dynamic networks (LDNs). The models consist of networks of subsystems with deterministic and uncertain dynamics subject to local and coupling constraints on the control and output signals. The distributed optimization algorithms are based on gradient-projection, subgradient, interior-point, and dual strategies that depend on the nature of the couplings and constraints of the underlying networks. The focus will be on a class of LDNs in which the dynamics of the subsystems are influenced by the control signals of the upstream subsystems with constraints on state and control variables. A distributed gradient-based algorithm is presented for implementing an interior-point method distributively with a network of agents, one for each subsystem.


Control Signal Model Predictive Control Coupling Constraint Subgradient Algorithm Model Predictive Control Strategy 
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.



This work was funded in part by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.


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Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Automation and Systems EngineeringFederal University of Santa CatarinaFlorianópolisBrazil

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