Distributed optimization: applications in model predictive control

Title: Distributed optimization: applications in model predictive control
Creator: Braun, Philipp; Grüne, Lars
Relation: Automatisierungstechnik Vol. 66, Issue 11, p. 939-949
Publisher Link: http://dx.doi.org/10.1515/auto-2018-0009
Publisher: Walter de Gruyter
Resource Type: journal article
Date: 2018
Description: 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.
Subject: model predictive control; distributed optimization; dual decomposition; alternating direction method of multipliers
Identifier: http://hdl.handle.net/1959.13/1410063
Identifier: uon:36120
Identifier: ISSN:0178-2312
Language: DE
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