Auto-regressive model based input and parameter estimation for nonlinear finite element models

Castiglione, Juan; Astroza, Rodrigo; Azam, Saeed Eftekhar; Linzell, Daniel G.

doi:10.1016/j.ymssp.2020.106779

Cited by 32 publications

(8 citation statements)

References 45 publications

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“…However, it is required that measurement equations contain all unknown forces, which imposes constraints on the locations of the deployed accelerometers. Moreover, when the seismic ground motion to a structure is unknown, the unknown ground acceleration is not presented in the measurement equations 38,39 . In this paper, GKF‐UI recently improved by the authors 42 is utilized to identify structural state and unknown inputs in Equation 18.…”

Section: The Proposed Identification Methodsmentioning

confidence: 99%

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Identification of model‐free hysteretic forces of magnetorheological dampers embedded in buildings under unknown excitations using incomplete structural responses

Lei

Yang

Huang

et al. 2021

Struct Control Health Monit

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Summary Magnetorheological (MR) damper is efficient to mitigate vibration of the structure subject to severe excitations. The identification of nonlinear characteristics of MR damper has attracted increasing attention. However, it is still challenging to identify model‐free hysteresis of MR dampers embedded in structures using only incomplete measurements of structural responses under unknown excitations. In this paper, an identification technique is proposed for this tough task, namely, nonparametric identification of MR nonlinear restoring forces in building structures under unknown excitations. The proposed technique involves identifications in two stages. In the first stage, the identification of linear parameters of bare structure and MR dampers under low‐level unknown excitations is conducted based on the generalized extended Kalman filter with unknown input (GEKF‐UI) by the authors. In the second stage, the hysteretic forces of MR dampers are proposed to be treated as “unknown fictitious forces” to the corresponding linear bare structure identified in the first stage. The generalized Kalman filter with unknown inputs (GKF‐UI) by the authors is adopted to identify all unknown inputs including the “unknown fictitious forces” originated from MR dampers. To demonstrate the proposed technique, some numerical examples are used to identify model‐free hysteresis of single or multi MR dampers with different nonlinear restoring force models in shear frames under unknown external force excitations or unknown seismic excitations, respectively. Furthermore, experimental testing of the identification of model‐free MR damper in a multi‐story shear frame under unknown external excitation is conducted.

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Section: The Proposed Identification Methodsmentioning

confidence: 99%

“…This imposes constraints on the number and location of the accelerometers required in acceleration measurements that must be available at the DOFs where the unknown external excitations are applied. For the identification under unknown seismic excitations, assumptions of unknown seismic inputs are needed in the existing approaches 38,39 …”

Section: Introductionmentioning

confidence: 99%

Identification of model‐free hysteretic forces of magnetorheological dampers embedded in buildings under unknown excitations using incomplete structural responses

Lei

Yang

Huang

et al. 2021

Struct Control Health Monit

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“…In the corrector phase, the estimated output covariance Pyy k+1|k and the estimated cross covariance Pθy k+1|k are used to compute the Kalman gain G k+1 needed to correct the propagated mean θk+1|k and covariance Pθθ k+1|k on the basis of the collected measurements y k+1 . The full expression of these quantities and the application of the UKF are detailed in Algorithm 1, adapted from [11].…”

Section: Unscented Kalman Filter For Parameter Estimationmentioning

confidence: 99%

Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics

Rosafalco

Azam

Manzoni

et al. 2021

The 1st International Electronic Conference on Algorithms

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System identification is often limited to parameter identification, while model uncertainties are disregarded or accounted for by a fictitious process noise. However, modelling assumptions may have a large impact on system identification. For this reason, we propose to use an unscented Kalman filter (UKF) empowered by online Bayesian model evidence computation for the sake of system identification and model selection. This approach employs more than one model to track the state of the system and associates with each model a plausibility measure, updated whenever new measurements are available. The filter outcomes obtained for different models are then compared and a quantitative confidence value is associated with each of them. Only the system identification outcomes related to the model with the highest plausibility are considered. While the coupling of extended Kalman filters (EKFs) and Bayesian model evidence was already addressed, we modify the approach to exploit the most striking features of the UKF, namely, the ease of implementation and higher-order accuracy in the description of the evolution of the state mean and variance. A challenging identification problem related to structural dynamics is discussed to show the effectiveness of the proposed methodology.

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“…Towards this need, Castiglione et al 14 proposed a time-varying autoregressive model for the unknown inputs and develop a strategy to simultaneously estimate them along with the parameters. However, the authors stated that due to the low-frequency drift in the estimated input, displacement responses could not be estimated satisfactorily.…”

Section: Introductionmentioning

confidence: 99%

Input–parameter–state estimation of limited information wind‐excited systems using a sequential Kalman filter

Impraimakis

Smyth

2022

Structural Contr & Hlth

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The estimation of the dynamic states, the parameters, and the input of systems subjected to wind loading is examined herein using a sequential Kalman filter.The procedure considers two Kalman filters in order to estimate initially the dynamic states and subsequently the system parameters along with the input, in an online fashion. The approach results in an accurate convergence as demonstrated by two linear systems with limited information and two nonlinear applications.

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Auto-regressive model based input and parameter estimation for nonlinear finite element models

Cited by 32 publications

References 45 publications

Identification of model‐free hysteretic forces of magnetorheological dampers embedded in buildings under unknown excitations using incomplete structural responses

Identification of model‐free hysteretic forces of magnetorheological dampers embedded in buildings under unknown excitations using incomplete structural responses

Unscented Kalman Filter Empowered by Bayesian Model Evidence for System Identification in Structural Dynamics

Input–parameter–state estimation of limited information wind‐excited systems using a sequential Kalman filter

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