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Licensed Unlicensed Requires Authentication Published by Oldenbourg Wissenschaftsverlag November 8, 2018

Verteilte Optimierung: Anwendungen in der Modellprädiktiven Regelung

Distributed optimization: applications in model predictive control
Philipp Braun and Lars Grüne

Zusammenfassung

Verteilte Optimierungsverfahren wie die duale Dekomposition oder die Alternating Direction Method of Multipliers (ADMM) erleben in den letzten Jahren ein erneutes steigendes Interesse in den unterschiedlichsten Anwendungen. Die zunehmende Vernetzung von Servern oder Mikrocontrollern weltweit sowie die Größe von heutigen Datensätzen liefern dabei die Grundlage für die Nachfrage nach iterativen, parallelisierbaren Optimierungsverfahren. In dieser Arbeit stellen wir verteilte Optimierungsalgorithmen und ihre Anwendungen bei der Berechnung von Zustandsrückführungen mithilfe der Modellprädiktiven Regelung vor. Wir konzentrieren uns auf die Systemdynamik sowie die Vernetzung der Systeme bei der Anwendbarkeit der Algorithmen. Darüber hinaus untersuchen wir die Algorithmen auf ihre Kommunikationsstruktur, den Austausch sensibler Daten, die Skalierbarkeit und die Flexibilität.

Abstract

Distributed optimization like dual decomposition or the alternating direction method of multipliers (ADMM), proposed centuries ago, experience an increased interest in various applications over the last years. Severs or microcontrollers connected all over the world and big data applications build the foundation and demand for iterative, parallelizable and distributed optimization algorithms. In this paper we present distributed optimization algorithms and their applications in the context of feedback design using model predictive control. We concentrate on the dynamics and the interconnection of the dynamical systems with respect to the applicability of the distributed optimization algorithms. Moreover, we focus on the communication structure in terms of the exchange of sensitive data, as well as the scalability and flexibility of the distributed optimization algorithms.

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Received: 2018-01-30
Accepted: 2018-07-12
Published Online: 2018-11-08
Published in Print: 2018-11-27

© 2018 Walter de Gruyter GmbH, Berlin/Boston