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A compendium of optimization algorithms for distributed linear-quadratic MPC
Citation Link: https://doi.org/10.15480/882.9190
Publikationstyp
Journal Article
Date Issued
2022-03-25
Sprache
English
Author(s)
TORE-DOI
Journal
Volume
70
Issue
4
Start Page
317
End Page
330
Citation
At-Automatisierungstechnik 70 (4): 317-330 (2022-03-25)
Publisher DOI
Scopus ID
Publisher
De Gruyter
Model Predictive Control (MPC) for {networked, cyber-physical, multi-agent} systems requires numerical methods to solve optimal control problems while meeting communication and real-time requirements. This paper presents an introduction on six distributed optimization algorithms and compares their properties in the context of distributed MPC for linear systems with convex quadratic objectives and polytopic constraints. In particular, dual decomposition, the alternating direction method of multipliers, a distributed active set method, an essentially decentralized interior point method, and Jacobi iterations are discussed. Numerical examples illustrate the challenges, the prospect, and the limits of distributed MPC with inexact solutions.
Subjects
active set methods
ADMM
conjugate gradients
distributed optimization
dual decomposition
fast gradients
interior point methods
Jacobi iterations
model predictive control
DDC Class
620: Engineering
Publication version
publishedVersion
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10.1515_auto-2021-0112.pdf
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Main Article
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529.46 KB
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