DocumentCode :
592271
Title :
Distributed ADMM for model predictive control and congestion control
Author :
Mota, Joao F. C. ; Xavier, Joao M. F. ; Aguiar, Pedro M. Q. ; Puschel, Markus
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear :
2012
fDate :
10-13 Dec. 2012
Firstpage :
5110
Lastpage :
5115
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.
Keywords :
data privacy; distributed algorithms; optimisation; predictive control; telecommunication congestion control; transport protocols; MPC; TCP/IP congestion control; alternating direction of multiplier; centralized solution; data processing; decentralized algorithm; distributed ADMM; distributed algorithm; distributed model predictive control; optimization problem; privacy; Actuators; Algorithm design and analysis; Bipartite graph; Clustering algorithms; Convergence; Gradient methods; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
Conference_Location :
Maui, HI
ISSN :
0743-1546
Print_ISBN :
978-1-4673-2065-8
Electronic_ISBN :
0743-1546
Type :
conf
DOI :
10.1109/CDC.2012.6426141
Filename :
6426141
Link To Document :
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