Distributed optimization for predictive control with input and state constraints: Preliminary theory and application to urban traffic control

Distributed model predictive control (DMPC) advocates the distribution of sensing and decision making to operate large, geographically distributed systems such as the power grid and traffic networks. This paper presents a distributed optimization framework for DMPC of linear dynamic networks with co...

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Bibliographic Details
Published in:	2009 IEEE International Conference on Systems, Man and Cybernetics pp. 3726 - 3732
Main Authors:	Camponogara, Eduardo, Scherer, Helton Fernando, Moura, Leonardo Vila
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-10-2009
Subjects:	Communication system traffic control Constraint optimization Constraint theory Decision making distributed MPC distributed optimization interior-point method Power grids Power system dynamics Power system modeling Predictive control Predictive models Traffic control urban traffic control
Online Access:	Get full text
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Summary:	Distributed model predictive control (DMPC) advocates the distribution of sensing and decision making to operate large, geographically distributed systems such as the power grid and traffic networks. This paper presents a distributed optimization framework for DMPC of linear dynamic networks with constraints on each network node. A linear dynamic network can be thought of as a directed graph, whose nodes have local dynamics that depend on the local and upstream control signals and are subject to constraints on state and control variables. The distributed algorithm is based on interior-point methods and can be shown to converge to a globally optimal solution. Some theoretical results are stated and a preliminary application to green-time control in urban traffic networks is described.
ISBN:	9781424427932 1424427932
ISSN:	1062-922X 2577-1655
DOI:	10.1109/ICSMC.2009.5346887