Application of extended, unscented, iterated extended and iterated unscented Kalman Filter for real-time structural identification

Askari, M; Li, J; Samali, B

Application of extended, unscented, iterated extended and iterated unscented Kalman Filter for real-time structural identification

Askari, M Li, J

Samali, B

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Publication Type:: Conference Proceeding
Citation:: Advances in Applied Mechanics Research, Conference Proceedings - 7th Australasian Congress on Applied Mechanics, ACAM 2012, 2012, pp. 1041 - 1051
Issue Date:: 2012-01-01

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Field	Value	Language
dc.contributor.author	Askari, M	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0002-2526-3048	en_US
dc.contributor.author	Samali, B	en_US
dc.date.issued	2012-01-01	en_US
dc.identifier.citation	Advances in Applied Mechanics Research, Conference Proceedings - 7th Australasian Congress on Applied Mechanics, ACAM 2012, 2012, pp. 1041 - 1051	en_US
dc.identifier.isbn	9781922107619	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120829
dc.description.abstract	System identification refers to any systematic way of deriving or improving models of dynamical systems through the use of experimental and field testing input-output data. In the field of civil engineering, identification of the state of a structure during service condition under dynamic loading, such as earthquake, in order to detect any damage as it occurs, has posed a great challenge to the research community. Therefore, online and real-time structural identification has attracted a great deal of attentions in the structural engineering research over the past decades, especially when input-output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used methods for estimation of states and parameters is the Kalman filter and its various nonlinear extensions like Extended Kalman Filter (EKF) and iterated Extended Kalman Filter (IEKF). However, these methods are not effective in the case of highly nonlinear problems. To overcome the problem, two filtering techniques, namely unscented Kalman filter (UKF) and iterated unscented Kalman filter (IUKF), have been recently developed to handle any functional nonlinearity. In this paper, an investigation has been carried out on the aforementioned methods for their effectiveness and efficiencies through a highly nonlinear SDOF structure as well as a two-storey linear structure. Results show that, although IEKF is an improved version of EKF, the IUKF, in most of cases, produces better results on state estimation and parameter identification than UKF and IEKF. IUKF is also more robust to measurement noise levels compared to the other approaches.	en_US
dc.relation.ispartof	Advances in Applied Mechanics Research, Conference Proceedings - 7th Australasian Congress on Applied Mechanics, ACAM 2012	en_US
dc.title	Application of extended, unscented, iterated extended and iterated unscented Kalman Filter for real-time structural identification	en_US
dc.type	Conference Proceeding
utslib.for	0905 Civil Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

System identification refers to any systematic way of deriving or improving models of dynamical systems through the use of experimental and field testing input-output data. In the field of civil engineering, identification of the state of a structure during service condition under dynamic loading, such as earthquake, in order to detect any damage as it occurs, has posed a great challenge to the research community. Therefore, online and real-time structural identification has attracted a great deal of attentions in the structural engineering research over the past decades, especially when input-output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used methods for estimation of states and parameters is the Kalman filter and its various nonlinear extensions like Extended Kalman Filter (EKF) and iterated Extended Kalman Filter (IEKF). However, these methods are not effective in the case of highly nonlinear problems. To overcome the problem, two filtering techniques, namely unscented Kalman filter (UKF) and iterated unscented Kalman filter (IUKF), have been recently developed to handle any functional nonlinearity. In this paper, an investigation has been carried out on the aforementioned methods for their effectiveness and efficiencies through a highly nonlinear SDOF structure as well as a two-storey linear structure. Results show that, although IEKF is an improved version of EKF, the IUKF, in most of cases, produces better results on state estimation and parameter identification than UKF and IEKF. IUKF is also more robust to measurement noise levels compared to the other approaches.

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http://hdl.handle.net/10453/120829