Parameter Estimation of Stochastic Fractional Dynamic Systems Using Nonlinear Bayesian Filtering System Identification Methods
Publication: Journal of Engineering Mechanics
Volume 150, Issue 2
Abstract
This paper presents the application of nonlinear Bayesian filtering–based system identification (SI) methods when employed to estimate the parameters of stochastic fractional dynamic systems. The objective is to demonstrate the capabilities and limitations of time-domain stochastic filtering–based SI for systems endowed with fractional derivative elements when the estimation is performed under different operating conditions. The conditions include measured forcing inputs (input-output identification), stochastic/unmeasured forcing inputs (output-only identification), and different types of measurements and levels of measurement noise, in the context of both linear and hysteretic fractional oscillators. The accuracy and estimation error of three methods was studied, namely, the unscented Kalman filter, the ensemble Kalman filter, and the particle filter. Baseline results that can be applied to the modeling, identification, and control of fractional structural and mechanical systems are provided. It is shown that nonlinear Bayesian filtering methods have the capability to accurately estimate the response and parameters of fractional oscillators, and that the coefficient and order of fractional elements are observable/identifiable from output response measurements.
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Data Availability Statement
All data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The first author was partially supported by the Ministerio de Educacion Superior Ciencia y Tecnologia (MESCYT) of the Dominican Republic through the FONDOCYT Project 2022-3A2-107. The support is gratefully acknowledged.
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Published In
Journal of Engineering Mechanics
Volume 150 • Issue 2 • February 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Jul 22, 2023
Accepted: Sep 16, 2023
Published online: Nov 23, 2023
Published in print: Feb 1, 2024
Discussion open until: Apr 23, 2024
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