Numerical benchmarking of dual decomposition-based optimization algorithms for distributed model predictive control

This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. These problems can be interpreted as multiple subsystems which are coupled through constraints on the availability of shared limited...

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Bibliographic Details
Published in:Results in control and optimization Vol. 17; p. 100495
Main Authors: Yfantis, Vassilios, Wagner, Achim, Ruskowski, Martin
Format: Journal Article
Language:English
Published: Elsevier B.V 01.12.2024
Elsevier
Subjects:
ADMM
Bundle method
Distributed model predictive control
Dual decomposition
Quasi-Newton
Subgradient method
Distributed model predictive control
Dual decomposition
Quasi-Newton
ADMM
Subgradient method
Bundle method
ISSN:2666-7207, 2666-7207
Online Access:Get full text
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Summary:This paper presents a benchmark study of dual decomposition-based distributed optimization algorithms applied to constraint-coupled model predictive control problems. These problems can be interpreted as multiple subsystems which are coupled through constraints on the availability of shared limited resources. In a dual decomposition-based framework the production and consumption of these resources can be coordinated by iteratively computing their prices and sharing them with the involved subsystems. Following a brief introduction to model predictive control different architectures and communication topologies for a distributed setting are presented. After decomposing the system-wide control problem into multiple subproblems by introducing dual variables, several distributed optimization algorithms, including the recently proposed quasi-Newton dual ascent algorithm, are discussed. Furthermore, an epigraph formulation of the bundle cuts as well as a line search strategy are proposed for the quasi-Newton dual ascent algorithm, which increase its numerical robustness and speed up its convergence compared to the previously used trust region. Finally, the quasi-Newton dual ascent algorithm is compared to the subgradient method, the bundle trust method and the alternating direction method of multipliers for a large number of benchmark problems. The used benchmark problems are publicly available on GitHub.
ISSN:2666-7207
2666-7207
DOI:10.1016/j.rico.2024.100495