Sensor Selection and State Estimation for Unobservable and Non-Linear System Models

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational...

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Autores principales:	Thijs Devos, Matteo Kirchner, Jan Croes, Wim Desmet, Frank Naets
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	extended Kalman filter state estimation sensor selection observability non-linear models Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/f66093cb71594879bd7c7d47601eab82
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spelling	oai:doaj.org-article:f66093cb71594879bd7c7d47601eab822021-11-25T18:56:53ZSensor Selection and State Estimation for Unobservable and Non-Linear System Models10.3390/s212274921424-8220https://doaj.org/article/f66093cb71594879bd7c7d47601eab822021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7492https://doaj.org/toc/1424-8220To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.Thijs DevosMatteo KirchnerJan CroesWim DesmetFrank NaetsMDPI AGarticleextended Kalman filterstate estimationsensor selectionobservabilitynon-linear modelsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7492, p 7492 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	extended Kalman filter state estimation sensor selection observability non-linear models Chemical technology TP1-1185
spellingShingle	extended Kalman filter state estimation sensor selection observability non-linear models Chemical technology TP1-1185 Thijs Devos Matteo Kirchner Jan Croes Wim Desmet Frank Naets Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
description	To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case.
format	article
author	Thijs Devos Matteo Kirchner Jan Croes Wim Desmet Frank Naets
author_facet	Thijs Devos Matteo Kirchner Jan Croes Wim Desmet Frank Naets
author_sort	Thijs Devos
title	Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
title_short	Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
title_full	Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
title_fullStr	Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
title_full_unstemmed	Sensor Selection and State Estimation for Unobservable and Non-Linear System Models
title_sort	sensor selection and state estimation for unobservable and non-linear system models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f66093cb71594879bd7c7d47601eab82
work_keys_str_mv	AT thijsdevos sensorselectionandstateestimationforunobservableandnonlinearsystemmodels AT matteokirchner sensorselectionandstateestimationforunobservableandnonlinearsystemmodels AT jancroes sensorselectionandstateestimationforunobservableandnonlinearsystemmodels AT wimdesmet sensorselectionandstateestimationforunobservableandnonlinearsystemmodels AT franknaets sensorselectionandstateestimationforunobservableandnonlinearsystemmodels
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Sensor Selection and State Estimation for Unobservable and Non-Linear System Models

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