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Distributed Model Predictive Consensus via the Alternating Direction Method of Multipliers

Author(s):

T.H. Summers, J. Lygeros
Conference/Journal:

Allerton Conference on Communication, Control, and Computing, Monticello, IL, USA
Abstract:

We propose a distributed optimization method for solving a distributed model predictive consensus problem. The goal is to design a distributed controller for a network of dynamical systems to optimize a coupled objective function while respecting state and input constraints. The distributed optimization method is an augmented Lagrangian method called the Alternating Direction Method of Multipliers (ADMM), which was introduced in the 1970s but has seen a recent resurgence in the context of dramatic increases in computing power and the development of widely available distributed computing platforms. The method is applied to position and velocity consensus in a network of double integrators. We find that a few tens of ADMM iterations yield closed-loop performance near what is achieved by solving the optimization problem centrally. Furthermore, the use of recent code generation techniques for solving local subproblems yields fast overall computation times.

Year:

2012
Type of Publication:

(01)Article
Supervisor:



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% Autogenerated BibTeX entry
@InProceedings { SumLyg:2012:IFA_4211,
    author={T.H. Summers and J. Lygeros},
    title={{Distributed Model Predictive Consensus via the Alternating
	  Direction Method of Multipliers}},
    booktitle={Allerton Conference on Communication, Control, and
	  Computing},
    pages={},
    year={2012},
    address={Monticello, IL, USA},
    month=oct,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=4211}
}
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