Design of Full State Observer Based on Data-Driven Dual System Representation

Ryosuke ADACHI; Yuji WAKASA

doi:10.1587/transfun.2022MAP0011

Design of Full State Observer Based on Data-Driven Dual System Representation

Ryosuke ADACHI, Yuji WAKASA

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E106-A No.5 pp.736-743

Publication Date: 2023/05/01

Publicized: 2022/10/24

Online ISSN: 1745-1337

DOI: 10.1587/transfun.2022MAP0011

Type of Manuscript: Special Section PAPER (Special Section on Mathematical Systems Science and its Applications)

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Authors

Ryosuke ADACHI
Yamaguchi University
Yuji WAKASA
Yamaguchi University

Keyword

observer design, data-driven estimation, linear matrix inequalities, dual systems

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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 -