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• W4221145145 abstract "This paper proposes a data-driven input reconstruction method from outputs (IRO) based on the Willems&#x2019; Fundamental Lemma. Given only output measurements, the unknown inputs estimated recursively by the IRO asymptotically converge to the true input without knowing the initial conditions. A recursive IRO and a moving-horizon IRO are developed based respectively on Lyapunov conditions and Luenberger-observer-type feedback, and their asymptotic convergence properties are studied. An experimental study is presented demonstrating the efficacy of the moving-horizon IRO for estimating the occupancy of a building on the EPFL campus via measured carbon dioxide levels." @default.
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• W4221145145 date "2022-01-01" @default.
• W4221145145 modified "2023-10-18" @default.
• W4221145145 title "Data-Driven Input Reconstruction and Experimental Validation" @default.
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• W4221145145 doi "https://doi.org/10.1109/lcsys.2022.3184904" @default.
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