The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article traite d'une méthode de conception d'observateur utilisant uniquement des données. Habituellement, l'observateur a besoin d'un modèle mathématique d'un système pour la prédiction d'état et le calcul du gain de l'observateur. Comme alternative à la prédiction basée sur un modèle, le prédicteur proposé calcule les états en utilisant une combinaison linéaire des données données. Pour concevoir le gain de l'observateur, les données qui représentent les systèmes doubles sont dérivées des données qui représentent le système d'origine. Les inégalités matricielles linéaires qui dépendent des données du système dual fournissent les gains de l'observateur.
Ryosuke ADACHI
Yamaguchi University
Yuji WAKASA
Yamaguchi University
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Ryosuke ADACHI, Yuji WAKASA, "Design of Full State Observer Based on Data-Driven Dual System Representation" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 5, pp. 736-743, May 2023, doi: 10.1587/transfun.2022MAP0011.
Abstract: This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022MAP0011/_p
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@ARTICLE{e106-a_5_736,
author={Ryosuke ADACHI, Yuji WAKASA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Design of Full State Observer Based on Data-Driven Dual System Representation},
year={2023},
volume={E106-A},
number={5},
pages={736-743},
abstract={This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.},
keywords={},
doi={10.1587/transfun.2022MAP0011},
ISSN={1745-1337},
month={May},}
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TY - JOUR
TI - Design of Full State Observer Based on Data-Driven Dual System Representation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 736
EP - 743
AU - Ryosuke ADACHI
AU - Yuji WAKASA
PY - 2023
DO - 10.1587/transfun.2022MAP0011
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2023
AB - This paper addresses an observer-design method only using data. Usually, the observer requires a mathematical model of a system for state prediction and observer gain calculation. As an alternative to the model-based prediction, the proposed predictor calculates the states using a linear combination of the given data. To design the observer gain, the data which represent dual systems are derived from the data which represent the original system. Linear matrix inequalities that depend on data of the dual system provides the observer gains.
ER -