Abstract
This paper presents a decentralized approach to certifying closed-loop passivity for a class of switched graph-based dynamic models. The switched modeling framework is particularly suited to power flow systems in which paths of power flow are switched on and off. Passivity is shown to be preserved under the interconnection of multiple such graph-based models, allowing for the formation of passive 'systems of systems.' Decentralized Model Predictive Controllers paired with each system can then be formulated with a passivity-preserving constraint to ensure closed-loop stability. This allows complex energy systems to be stabilized with decentralized or distributed control architectures, while centralized control may not be practical due to the inherent computational complexity or communication bandwidth limitations. A numerical example demonstrates the efficacy of the proposed approach on a fluid tank system.
Original language | English (US) |
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Title of host publication | 2018 Annual American Control Conference, ACC 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 198-203 |
Number of pages | 6 |
ISBN (Print) | 9781538654286 |
DOIs | |
State | Published - Aug 9 2018 |
Externally published | Yes |
Event | 2018 Annual American Control Conference, ACC 2018 - Milwauke, United States Duration: Jun 27 2018 → Jun 29 2018 |
Publication series
Name | Proceedings of the American Control Conference |
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Volume | 2018-June |
ISSN (Print) | 0743-1619 |
Other
Other | 2018 Annual American Control Conference, ACC 2018 |
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Country/Territory | United States |
City | Milwauke |
Period | 6/27/18 → 6/29/18 |
Bibliographical note
Funding Information:*This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant Number DGE-1144245, the National Science Foundation Engineering Research Center for Power Optimization of Electro-Thermal Systems (POETS) with cooperative agreement EEC-1449548, and the Air Force Research Laboratory.
Publisher Copyright:
© 2018 AACC.