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at - Automatisierungstechnik

Methoden und Anwendungen der Steuerungs-, Regelungs- und Informationstechnik

[AT - Automation Technology: Methods and Applications of Control, Regulation, and Information Technology
]

Editor-in-Chief: Jumar, Ulrich


IMPACT FACTOR 2017: 0.503

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2196-677X
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Volume 66, Issue 11

Issues

Verteilte Optimierung: Anwendungen in der Modellprädiktiven Regelung

Distributed optimization: applications in model predictive control

Philipp Braun / Lars Grüne
Published Online: 2018-11-08 | DOI: https://doi.org/10.1515/auto-2018-0009

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.

Schlagwörter: modellprädiktive Regelung; verteilte Optimierung; duale Dekomposition; alternating direction method of multipliers

Keywords: model predictive control; distributed optimization; dual decomposition; alternating diection method of multipliers

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About the article

Philipp Braun

Philipp Braun ist seit 2016 Senior Research Associate an der University of Newcastle, Australia, in der School of Electrical Engineering and Computing. Nach einem Mathematikstudium an der Technischen Universität Kaiserslautern (Dipl.-Math., 2012) promovierte er an der Universität Bayreuth (Dr. rer. nat., 2016). Als Wissenschaftler nach der Promotion war er als Akademischer Rat an der Universität Bayreuth und an der University of Newcastle tätig.Seine Forschungsinteressen liegen im Gebiet der Kontrolltheorie. Hierbei beschäftigt er sich insbesondere mit verteilten Optimierungsverfahren in der Modellprädiktiven Regelung sowie mit Lyapunov-Funktionen zur Stabilisierung dynamischer Systeme.

Lars Grüne

Lars Grüne ist seit 2002 Professor für Angewandte Mathematik an der Universität Bayreuth. Er wurde 1996 an der Universität Augsburg promoviert und habilitierte sich 2001 an der Goethe-Universität Frankfurt/Main, jeweils im Fach Mathematik. Er war als Gastwissenschafter an den Universitäten Rom ‘Sapienza’ (Italien), Padua (Italien), Melbourne (Australien), Paris IX – Dauphine (Frankreich) und Newcastle (Australien) tätig. Prof. Grüne ist Editor-in-Chief der Zeitschrift Mathematics of Control, Signals and Systems (MCSS) und Associate Editor bei verschiedenen weiteren Zeitschriften, z. B. beim Journal of Optimization Theory and Applications (JOTA), Mathematical Control and Related Fields (MCRF) und den IEEE Control Systems Letters (CSS-L). Seine Forschungsinteressen liegen im Gebiet der Mathematischen System- und Kontrolltheorie mit Schwerpunkt auf numerischen und optimierungsbasierten Methoden für nichtlineare Systeme.


Received: 2018-01-30

Accepted: 2018-07-12

Published Online: 2018-11-08

Published in Print: 2018-11-27


Citation Information: at - Automatisierungstechnik, Volume 66, Issue 11, Pages 939–949, ISSN (Online) 2196-677X, ISSN (Print) 0178-2312, DOI: https://doi.org/10.1515/auto-2018-0009.

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