Pseudo distributed optimal state estimation for a class of networked systems

This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state pred...
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Autor*in:	Yuanzhe Wang [verfasserIn] Hongjie Hu

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	recursive state estimation Optimization techniques Distributed estimation Network operating systems Dynamical systems Kalman filter Estimating techniques networked system

Übergeordnetes Werk:	Enthalten in: Transactions of the Institute of Measurement and Control - London : Inst., 1980, 37(2015), 10, Seite 1232
Übergeordnetes Werk:	volume:37 ; year:2015 ; number:10 ; pages:1232

Links:	Volltext Link aufrufen

DOI / URN:	10.1177/0142331214560804

Katalog-ID:	OLC1956387773

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allfields	10.1177/0142331214560804 doi PQ20160617 (DE-627)OLC1956387773 (DE-599)GBVOLC1956387773 (PRQ)c2174-1ab7501ba9717aa5500e8b902b17e577cddeba1950be4032dcde54d74c2bfdfd0 (KEY)0117647120150000037001001232pseudodistributedoptimalstateestimationforaclassof DE-627 ger DE-627 rakwb eng 620 DE-600 50.21 bkl 50.23 bkl Yuanzhe Wang verfasserin aut Pseudo distributed optimal state estimation for a class of networked systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter. recursive state estimation Optimization techniques Distributed estimation Network operating systems Dynamical systems Kalman filter Estimating techniques networked system Hongjie Hu oth Enthalten in Transactions of the Institute of Measurement and Control London : Inst., 1980 37(2015), 10, Seite 1232 (DE-627)129616400 (DE-600)244053-2 (DE-576)015114368 0142-3312 nnns volume:37 year:2015 number:10 pages:1232 http://dx.doi.org/10.1177/0142331214560804 Volltext http://search.proquest.com/docview/1716332459 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 50.21 AVZ 50.23 AVZ AR 37 2015 10 1232
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allfields_unstemmed	10.1177/0142331214560804 doi PQ20160617 (DE-627)OLC1956387773 (DE-599)GBVOLC1956387773 (PRQ)c2174-1ab7501ba9717aa5500e8b902b17e577cddeba1950be4032dcde54d74c2bfdfd0 (KEY)0117647120150000037001001232pseudodistributedoptimalstateestimationforaclassof DE-627 ger DE-627 rakwb eng 620 DE-600 50.21 bkl 50.23 bkl Yuanzhe Wang verfasserin aut Pseudo distributed optimal state estimation for a class of networked systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter. recursive state estimation Optimization techniques Distributed estimation Network operating systems Dynamical systems Kalman filter Estimating techniques networked system Hongjie Hu oth Enthalten in Transactions of the Institute of Measurement and Control London : Inst., 1980 37(2015), 10, Seite 1232 (DE-627)129616400 (DE-600)244053-2 (DE-576)015114368 0142-3312 nnns volume:37 year:2015 number:10 pages:1232 http://dx.doi.org/10.1177/0142331214560804 Volltext http://search.proquest.com/docview/1716332459 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 50.21 AVZ 50.23 AVZ AR 37 2015 10 1232
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allfieldsSound	10.1177/0142331214560804 doi PQ20160617 (DE-627)OLC1956387773 (DE-599)GBVOLC1956387773 (PRQ)c2174-1ab7501ba9717aa5500e8b902b17e577cddeba1950be4032dcde54d74c2bfdfd0 (KEY)0117647120150000037001001232pseudodistributedoptimalstateestimationforaclassof DE-627 ger DE-627 rakwb eng 620 DE-600 50.21 bkl 50.23 bkl Yuanzhe Wang verfasserin aut Pseudo distributed optimal state estimation for a class of networked systems 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter. recursive state estimation Optimization techniques Distributed estimation Network operating systems Dynamical systems Kalman filter Estimating techniques networked system Hongjie Hu oth Enthalten in Transactions of the Institute of Measurement and Control London : Inst., 1980 37(2015), 10, Seite 1232 (DE-627)129616400 (DE-600)244053-2 (DE-576)015114368 0142-3312 nnns volume:37 year:2015 number:10 pages:1232 http://dx.doi.org/10.1177/0142331214560804 Volltext http://search.proquest.com/docview/1716332459 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 50.21 AVZ 50.23 AVZ AR 37 2015 10 1232
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author	Yuanzhe Wang
spellingShingle	Yuanzhe Wang ddc 620 bkl 50.21 bkl 50.23 misc recursive state estimation misc Optimization techniques misc Distributed estimation misc Network operating systems misc Dynamical systems misc Kalman filter misc Estimating techniques misc networked system Pseudo distributed optimal state estimation for a class of networked systems
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title_sort	pseudo distributed optimal state estimation for a class of networked systems
title_auth	Pseudo distributed optimal state estimation for a class of networked systems
abstract	This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter.
abstractGer	This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter.
abstract_unstemmed	This paper deals with distributed state estimation for a class of networked systems, which extends the results of Zhou (Coordinated one-step optimal distributed state prediction for a networked dynamical system, IEEE Transactions on Automatic Control , 58(11): 2756-2771, 2013) on one-step state predictions. Through introducing a specific discrete state-space representation of the distributed estimator, whose number of internal inputs and outputs are twice as that of the plant, recursive and explicit expressions are derived respectively for the optimal gain matrix and the covariance matrix. Their expressions have similar structures to those of the state predictor obtained in Zhou (2013), which means that the desired state estimator inherits the computational advantages of the state predictor, and is therefore more suitable for systems consisting of a large number of subsystems than the lumped Kalman filter. Numerical simulations show that the state estimator developed in this paper may even have the same estimation accuracy as that of the lumped Kalman filter.
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title_short	Pseudo distributed optimal state estimation for a class of networked systems
url	http://dx.doi.org/10.1177/0142331214560804 http://search.proquest.com/docview/1716332459
remote_bool	false
author2	Hongjie Hu
author2Str	Hongjie Hu
ppnlink	129616400
mediatype_str_mv	n
isOA_txt	false
hochschulschrift_bool	false
author2_role	oth
doi_str	10.1177/0142331214560804
up_date	2024-07-03T20:19:59.265Z
_version_	1803590565117296640
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