Uncertainty Learning for LTI Systems with Stability Guarantees

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Samenvatting

We present a framework for learning of modeling uncertainties in Linear Time Invariant (LTI) systems to improve the predictive capacity of system models in the input-output sense. First, we propose a methodology to extend the LTI model with an uncertainty model. The proposed framework guarantees stability of the extended model. To achieve this, two semi-definite programs are provided that allow obtaining optimal uncertainty model parameters, given state and uncertainty data. Second, to obtain this data from available input-output trajectory data, we introduce a filter in which an internal model of the uncertainty is proposed. This filter is also designed via a semi-definite program with guaranteed robustness with respect to uncertainty model mismatches, disturbances, and noise. Numerical simulations are presented to illustrate the effectiveness and practicality of the proposed methodology in improving model accuracy, while guaranteeing model stability.
Originele taal-2Engels
Titel2024 European Control Conference, ECC 2024
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's2568-2573
Aantal pagina's6
ISBN van elektronische versie978-3-9071-4410-7
DOI's
StatusGepubliceerd - 24 jul. 2024
Evenement22nd European Control Conference 2024, ECC 2024 - KTH Royal Institute of Technology, Stockholm, Zweden
Duur: 25 jun. 202428 jun. 2024
Congresnummer: 22
https://ecc24.euca-ecc.org/

Congres

Congres22nd European Control Conference 2024, ECC 2024
Verkorte titelECC 2024
Land/RegioZweden
StadStockholm
Periode25/06/2428/06/24
Internet adres

Financiering

This publication is part of the project Digital Twin project 4.3 with project number P18-03 of the research programme Perspectief which is (mainly) financed by the Dutch Research Council (NWO).

FinanciersFinanciernummer
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

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