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Efficient Robust Model Predictive Control using Chordality
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
C3 IoT, CA USA.
2019 (English)In: 2019 18TH EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2019, p. 4270-4275Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we show that chordal structure can be used to devise efficient optimization methods for robust model predictive control problems. To this end, first the problem is converted to an equivalent robust quadratic programming formulation. We then illustrate how the chordal structure can be used to distribute the computations in a primal-dual interior-point method among computational agents, which in turn allows us to accelerate the algorithm by efficient parallel computations. We investigate performance of the framework in Julia using numerical examples.

Place, publisher, year, edition, pages
IEEE , 2019. p. 4270-4275
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-161420DOI: 10.23919/ECC.2019.8796011ISI: 000490488304050ISBN: 978-3-907144-00-8 (print)OAI: oai:DiVA.org:liu-161420DiVA, id: diva2:1366899
Conference
18th European Control Conference (ECC)
Note

Funding Agencies|ELLIIT; Wallenberg Artificial Intelligence, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-10-31

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