A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropri...

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Autores principales:	Mohamadreza Sheibani, Ge Ou
Formato:	article
Lenguaje:	EN
Publicado:	SAGE Publishing 2021
Materias:	Control engineering systems. Automatic machinery (General) TJ212-225 Acoustics. Sound QC221-246
Acceso en línea:	https://doaj.org/article/3303672e0083406dbfe4594f6f84bbca
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id	oai:doaj.org-article:3303672e0083406dbfe4594f6f84bbca
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spelling	oai:doaj.org-article:3303672e0083406dbfe4594f6f84bbca2021-12-02T01:34:31ZA Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures1461-34842048-404610.1177/14613484211014316https://doaj.org/article/3303672e0083406dbfe4594f6f84bbca2021-12-01T00:00:00Zhttps://doi.org/10.1177/14613484211014316https://doaj.org/toc/1461-3484https://doaj.org/toc/2048-4046The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.Mohamadreza SheibaniGe OuSAGE PublishingarticleControl engineering systems. Automatic machinery (General)TJ212-225Acoustics. SoundQC221-246ENJournal of Low Frequency Noise, Vibration and Active Control, Vol 40 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Control engineering systems. Automatic machinery (General) TJ212-225 Acoustics. Sound QC221-246
spellingShingle	Control engineering systems. Automatic machinery (General) TJ212-225 Acoustics. Sound QC221-246 Mohamadreza Sheibani Ge Ou A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
description	The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.
format	article
author	Mohamadreza Sheibani Ge Ou
author_facet	Mohamadreza Sheibani Ge Ou
author_sort	Mohamadreza Sheibani
title	A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
title_short	A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
title_full	A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
title_fullStr	A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
title_full_unstemmed	A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures
title_sort	bayesian optimized framework for successful application of unscented kalman filter in parameter identification of mdof structures
publisher	SAGE Publishing
publishDate	2021
url	https://doaj.org/article/3303672e0083406dbfe4594f6f84bbca
work_keys_str_mv	AT mohamadrezasheibani abayesianoptimizedframeworkforsuccessfulapplicationofunscentedkalmanfilterinparameteridentificationofmdofstructures AT geou abayesianoptimizedframeworkforsuccessfulapplicationofunscentedkalmanfilterinparameteridentificationofmdofstructures AT mohamadrezasheibani bayesianoptimizedframeworkforsuccessfulapplicationofunscentedkalmanfilterinparameteridentificationofmdofstructures AT geou bayesianoptimizedframeworkforsuccessfulapplicationofunscentedkalmanfilterinparameteridentificationofmdofstructures
_version_	1718402949274664960

A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

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