Distributed ADMM for model predictive control and congestion control

João F. C. Mota; João M. F. Xavier; Pedro M. Q. Aguiar; Markus Püschel

doi:10.1109/CDC.2012.6426141

Distributed ADMM for model predictive control and congestion control

João F. C. Mota^*, João M. F. Xavier, Pedro M. Q. Aguiar, Markus Püschel

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

55 Citations (Scopus)

Abstract

Many problems in control can be modeled as an optimization problem over a network of nodes. Solving them with distributed algorithms provides advantages over centralized solutions, such as privacy and the ability to process data locally. In this paper, we solve optimization problems in networks where each node requires only partial knowledge of the problem's solution. We explore this feature to design a decentralized algorithm that allows a significant reduction in the total number of communications. Our algorithm is based on the Alternating Direction of Multipliers (ADMM), and we apply it to distributed Model Predictive Control (MPC) and TCP/IP congestion control. Simulation results show that the proposed algorithm requires less communications than previous work for the same solution accuracy.

Original language	English
Title of host publication	51st IEEE Annual Conference on Decision and Control 2012
Publisher	IEEE
Pages	5110-5115
Number of pages	6
ISBN (Electronic)	9781467320665
ISBN (Print)	9781467320658
DOIs	https://doi.org/10.1109/CDC.2012.6426141
Publication status	Published - 2012

ASJC Scopus subject areas

Control and Systems Engineering
Modelling and Simulation
Control and Optimization

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10.1109/CDC.2012.6426141

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title = "Distributed ADMM for model predictive control and congestion control",

abstract = "Many problems in control can be modeled as an optimization problem over a network of nodes. Solving them with distributed algorithms provides advantages over centralized solutions, such as privacy and the ability to process data locally. In this paper, we solve optimization problems in networks where each node requires only partial knowledge of the problem's solution. We explore this feature to design a decentralized algorithm that allows a significant reduction in the total number of communications. Our algorithm is based on the Alternating Direction of Multipliers (ADMM), and we apply it to distributed Model Predictive Control (MPC) and TCP/IP congestion control. Simulation results show that the proposed algorithm requires less communications than previous work for the same solution accuracy.",

author = "Mota, {Jo{\~a}o F. C.} and Xavier, {Jo{\~a}o M. F.} and Aguiar, {Pedro M. Q.} and Markus P{\"u}schel",

year = "2012",

doi = "10.1109/CDC.2012.6426141",

language = "English",

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T1 - Distributed ADMM for model predictive control and congestion control

AU - Mota, João F. C.

AU - Xavier, João M. F.

AU - Aguiar, Pedro M. Q.

AU - Püschel, Markus

PY - 2012

Y1 - 2012

N2 - Many problems in control can be modeled as an optimization problem over a network of nodes. Solving them with distributed algorithms provides advantages over centralized solutions, such as privacy and the ability to process data locally. In this paper, we solve optimization problems in networks where each node requires only partial knowledge of the problem's solution. We explore this feature to design a decentralized algorithm that allows a significant reduction in the total number of communications. Our algorithm is based on the Alternating Direction of Multipliers (ADMM), and we apply it to distributed Model Predictive Control (MPC) and TCP/IP congestion control. Simulation results show that the proposed algorithm requires less communications than previous work for the same solution accuracy.

AB - Many problems in control can be modeled as an optimization problem over a network of nodes. Solving them with distributed algorithms provides advantages over centralized solutions, such as privacy and the ability to process data locally. In this paper, we solve optimization problems in networks where each node requires only partial knowledge of the problem's solution. We explore this feature to design a decentralized algorithm that allows a significant reduction in the total number of communications. Our algorithm is based on the Alternating Direction of Multipliers (ADMM), and we apply it to distributed Model Predictive Control (MPC) and TCP/IP congestion control. Simulation results show that the proposed algorithm requires less communications than previous work for the same solution accuracy.

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U2 - 10.1109/CDC.2012.6426141

DO - 10.1109/CDC.2012.6426141

M3 - Conference contribution

AN - SCOPUS:84874223032

SN - 9781467320658

SP - 5110

EP - 5115

BT - 51st IEEE Annual Conference on Decision and Control 2012

PB - IEEE

ER -

Distributed ADMM for model predictive control and congestion control

Abstract

ASJC Scopus subject areas

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