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Robust approximate symbolic models for a class of continuous-time uncertain nonlinear systems via a control interface
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-6046-7129
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-7309-8086
2022 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 145, p. 110520-, article id 110520Article in journal (Refereed) Published
Abstract [en]

Discrete abstractions have become a standard approach to assist control synthesis under complex specifications. Most techniques for the construction of a discrete abstraction for a continuous-time system require time-space discretization of the concrete system, which constitutes property satisfaction for the continuous-time system non-trivial. In this work, we aim at relaxing this requirement by introducing a control interface. Firstly, we connect the continuous-time uncertain concrete system with its discrete deterministic state-space abstraction with a control interface. Then, a novel stability notion called eta-approximately controlled globally practically stable, and a new simulation relation called robust approximate simulation relation are proposed. It is shown that the uncertain concrete system, under the condition that there exists an admissible control interface such that the augmented system (composed of the concrete system and its abstraction) can be made eta-approximately controlled globally practically stable, robustly approximately simulates its discrete abstraction. The effectiveness of the proposed results is illustrated by two simulation examples.

Place, publisher, year, edition, pages
Elsevier BV , 2022. Vol. 145, p. 110520-, article id 110520
Keywords [en]
Discrete abstraction, Uncertain systems, Robust approximate simulation relation, Control interface
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-322473DOI: 10.1016/j.automatica.2022.110520ISI: 000888559100006Scopus ID: 2-s2.0-85135904674OAI: oai:DiVA.org:kth-322473DiVA, id: diva2:1719952
Note

QC 20221216

Available from: 2022-12-16 Created: 2022-12-16 Last updated: 2022-12-16Bibliographically approved

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Yu, PianDimarogonas, Dimos V.

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