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Nonlinear control of large scale complex systems using convex optimization tools and self-adaptation

Rovas Dimitrios, E.B. Kosmatopoulos, K. Aboudolas, S. Baldi

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URIhttp://purl.tuc.gr/dl/dias/FAD06340-B43E-4970-AE04-6F1172ECD1F3-
Identifierhttps://doi.org/10.1109/CDC.2011.6160258-
Languageen-
Extent6 pagesen
TitleNonlinear control of large scale complex systems using convex optimization tools and self-adaptationen
CreatorRovas Dimitriosen
CreatorΡοβας Δημητριοςel
CreatorE.B. Kosmatopoulosen
CreatorK. Aboudolasen
CreatorS. Baldien
PublisherInstitute of Electrical and Electronics Engineersen
Content SummaryBased on recent advances on convex design for Large-Scale Control Systems (LSCSs) and robust and efficient LSCS self-tuning/adaptation, a methodology is proposed in this paper which aims at providing an integrated LSCS-design, applicable to large-scale systems of arbitrary scale, heterogeneity and complexity and capable of: 1) Providing stable, efficient and arbitrarily-close-to-optimal LSCS performance; 2) Being able to incorporate a variety of constraints, including limited control constraints as well as constraints that are nonlinear functions of the system controls and states; 3) Being intrinsically self-tunable, able to rapidly and efficiently optimize LSCS performance when short-, medium- or long-time variations affect the large-scale system; 4) Achieving the above, while being scalable and modular. The purpose of the present paper is to provide the main features of the proposed control design methodology.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2015-10-17-
Date of Publication2011-
Bibliographic CitationS. Baldi, E.B. Kosmatopoulos, K. Aboudolas, D. Rovas, A. Papachristodoulou," Nonlinear control of large scale complex systems using convex optimization tools and self-adaptation," in 2011 50th IEEE Conf. on Decision and Control and Eur. Control (CDC-ECC) ,pp.5407 - 5412. doi:10.1109/CDC.2011.6160258en

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