Combined state and parameter identification of nonlinear structural dynamical systems based on Rao-Blackwellization and Markov chain Monte Carlo simulations

Abhinav, S and Manohar, C S (2017) Combined state and parameter identification of nonlinear structural dynamical systems based on Rao-Blackwellization and Markov chain Monte Carlo simulations. In: MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 102 . pp. 364-381.

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Abstract

The problem of combined state and parameter estimation in nonlinear state space models, based on Bayesian filtering methods, is considered. A novel approach, which combines Rao-Blacicwellized particle filters for state estimation with Markov chain Monte Carlo (MCMC) simulations for parameter identification, is proposed. In order to ensure successful performance of the MCMC samplers, in situations involving large amount of dynamic measurement data and (or) low measurement noise, the study employs a modified measurement model combined with an importance sampling based correction. The parameters of the process noise covariance matrix are also included as quantities to be identified. The study employs the Rao-Blackwellization step at two stages: one, associated with the state estimation problem in the particle filtering step, and, secondly, in the evaluation of the ratio of likelihoods in the MCMC run. The satisfactory performance of the proposed method is illustrated on three dynamical systems: (a) a computational model of a nonlinear beam moving oscillator system, (b) a laboratory scale beam traversed by a loaded trolley, and (c) an earthquake shake table study on a bending-torsion coupled nonlinear frame subjected to uniaxial support motion. (C) 2017 Elsevier Ltd. All rights reserved.

Item Type:	Journal Article
Publication:	MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Additional Information:	Copy right for this article belongs to the ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD, 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND
Department/Centre:	Division of Mechanical Sciences > Civil Engineering
Date Deposited:	24 Nov 2017 10:21
Last Modified:	24 Nov 2017 10:21
URI:	http://eprints.iisc.ac.in/id/eprint/58288

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