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-2 | Engels |
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Titel | 2024 European Control Conference, ECC 2024 |
Uitgeverij | Institute of Electrical and Electronics Engineers |
Pagina's | 2568-2573 |
Aantal pagina's | 6 |
ISBN van elektronische versie | 978-3-9071-4410-7 |
DOI's | |
Status | Gepubliceerd - 24 jul. 2024 |
Evenement | 22nd European Control Conference 2024, ECC 2024 - KTH Royal Institute of Technology, Stockholm, Zweden Duur: 25 jun. 2024 → 28 jun. 2024 Congresnummer: 22 https://ecc24.euca-ecc.org/ |
Congres
Congres | 22nd European Control Conference 2024, ECC 2024 |
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Verkorte titel | ECC 2024 |
Land/Regio | Zweden |
Stad | Stockholm |
Periode | 25/06/24 → 28/06/24 |
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).
Financiers | Financiernummer |
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Nederlandse Organisatie voor Wetenschappelijk Onderzoek |