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Hauptverfasser: Farina, Marcello, Ferrari-Trecate, Giancarlo, Scattolini, Riccardo
Format: Preprint
Veröffentlicht: 2024
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Online-Zugang:https://arxiv.org/abs/2401.17933
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author Farina, Marcello
Ferrari-Trecate, Giancarlo
Scattolini, Riccardo
author_facet Farina, Marcello
Ferrari-Trecate, Giancarlo
Scattolini, Riccardo
contents This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, conditions for the convergence of the estimation error to zero are analyzed.
format Preprint
id arxiv_https___arxiv_org_abs_2401_17933
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Moving horizon partition-based state estimation of large-scale systems -- Revised version
Farina, Marcello
Ferrari-Trecate, Giancarlo
Scattolini, Riccardo
Systems and Control
This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i.e. systems decomposed into coupled subsystems with non-overlapping states. The MHE approach is used due to its capability of exploiting physical constraints on states in the estimation process. In the proposed algorithms, each subsystem solves reduced-order MHE problems to estimate its own state and different estimators have different computational complexity, accuracy and transmission requirements among subsystems. In all cases, conditions for the convergence of the estimation error to zero are analyzed.
title Moving horizon partition-based state estimation of large-scale systems -- Revised version
topic Systems and Control
url https://arxiv.org/abs/2401.17933