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Observer Design for the State Estimation of Epidemic Processes
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control). (mubniazi@kth.se)
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).ORCID iD: 0000-0001-9940-5929
2022 (English)In: 2022 IEEE 61ST CONFERENCE ON DECISION AND CONTROL (CDC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 4325-4332Conference paper, Published paper (Refereed)
Abstract [en]

Although an appropriate choice of measured state variables may ensure observability, designing state observers for the state estimation of epidemic models remains a challenging task. Epidemic spread is a nonlinear process, often modeled as the law of mass action, which is of a quadratic form; thus, on a compact domain, its Lipschitz constant turns out to be local and relatively large, which renders the Lipschitz-based design criteria of existing observer architectures infeasible. In this paper, a novel observer architecture is proposed for the state estimation of a class of nonlinear systems that encompasses the deterministic epidemic models. The proposed observer offers extra leverage to reduce the influence of nonlinearity in the estimation error dynamics, which is not possible in other Luenberger-like observers. Algebraic Riccati inequalities are derived as sufficient conditions for the asymptotic convergence of the estimation error to zero under local Lipschitz and generalized Lipschitz assumptions. Equivalent linear matrix inequality formulations of the algebraic Riccati inequalities are also provided. The efficacy of the proposed observer design is illustrated by its application on the celebrated SIDARTHE-V epidemic model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 4325-4332
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords [en]
Nonlinear systems, epidemic processes, state estimation, observer design
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-326384DOI: 10.1109/CDC51059.2022.9993275ISI: 000948128103100Scopus ID: 2-s2.0-85146993863OAI: oai:DiVA.org:kth-326384DiVA, id: diva2:1754285
Conference
IEEE 61st Conference on Decision and Control (CDC), DEC 06-09, 2022, Cancun, MEXICO
Note

QC 20230503

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2023-05-03Bibliographically approved

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Niazi, Muhammad Umar B.Johansson, Karl H.

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  • apa
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Output format
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