Distributed ADMM for model predictive control and congestion control

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

42 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 languageEnglish
Title of host publication2012 IEEE 51st Annual Conference on Decision and Control
Pages5110-5115
Number of pages6
ISBN (Electronic)978-1-4673-2066-5
DOIs
Publication statusPublished - 2012

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization

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  • Cite this

    Mota, J. F. C., Xavier, J. M. F., Aguiar, P. M. Q., & Püschel, M. (2012). Distributed ADMM for model predictive control and congestion control. In 2012 IEEE 51st Annual Conference on Decision and Control (pp. 5110-5115) https://doi.org/10.1109/CDC.2012.6426141