Advanced

Dynamic Dual Decomposition for Distributed Control

Rantzer, Anders LU (2009) American Control Conference 2009 p.884-888
Abstract
We show how dynamic price mechanisms can be used for decomposition and distributed optimization of control systems.



A classical method to deal with optimization constraints is Lagrange relaxation, where dual variables are introduced in the optimization objective. When variables of different subproblems are connected by such constraints, the dual variables can be interpreted as prices in a market mechanism serving to achieve mutual agreement between the subproblems. In this paper, the same idea is used for decomposition of optimal control problems, with dynamics in both decision variables and prices. We show how the prices can be used for decentralized verification that a control law or trajectory stays within a... (More)
We show how dynamic price mechanisms can be used for decomposition and distributed optimization of control systems.



A classical method to deal with optimization constraints is Lagrange relaxation, where dual variables are introduced in the optimization objective. When variables of different subproblems are connected by such constraints, the dual variables can be interpreted as prices in a market mechanism serving to achieve mutual agreement between the subproblems. In this paper, the same idea is used for decomposition of optimal control problems, with dynamics in both decision variables and prices. We show how the prices can be used for decentralized verification that a control law or trajectory stays within a prespecified distance from optimality. For example, approximately optimal decentralized controllers can be obtained by using simplified models for decomposition and more accurate local models for control. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to conference
publication status
published
subject
pages
884 - 888
conference name
American Control Conference 2009
external identifiers
  • WOS:000270044900146
  • Scopus:70449672733
project
LCCC-distributed
AEOLUS
CHAT
language
English
LU publication?
yes
id
a3dc38f3-3f66-4a5e-9b2e-de34e61ebc48 (old id 1453983)
date added to LUP
2009-08-05 09:02:59
date last changed
2016-11-27 04:36:42
@misc{a3dc38f3-3f66-4a5e-9b2e-de34e61ebc48,
  abstract     = {We show how dynamic price mechanisms can be used for decomposition and distributed optimization of control systems.<br/><br>
<br/><br>
A classical method to deal with optimization constraints is Lagrange relaxation, where dual variables are introduced in the optimization objective. When variables of different subproblems are connected by such constraints, the dual variables can be interpreted as prices in a market mechanism serving to achieve mutual agreement between the subproblems. In this paper, the same idea is used for decomposition of optimal control problems, with dynamics in both decision variables and prices. We show how the prices can be used for decentralized verification that a control law or trajectory stays within a prespecified distance from optimality. For example, approximately optimal decentralized controllers can be obtained by using simplified models for decomposition and more accurate local models for control.},
  author       = {Rantzer, Anders},
  language     = {eng},
  pages        = {884--888},
  title        = {Dynamic Dual Decomposition for Distributed Control},
  year         = {2009},
}