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

Marios Impraimakis; Andrew W. Smyth

doi:10.1002/stc.2919

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

Marios Impraimakis, Andrew W. Smyth

Columbia University

Research output: Contribution to journal › Article › peer-review

14 Citations (SciVal)

Abstract

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.

Original language	English
Article number	e2919
Journal	Structural Control and Health Monitoring
Volume	29
Issue number	4
Early online date	4 Jan 2022
DOIs	https://doi.org/10.1002/stc.2919
Publication status	Published - 30 Apr 2022

Data Availability Statement

Data available on request from the authors

Funding

U.S. National Science Foundation. Grant Number: CMMI-1563364

Keywords

input–parameter–state estimation
limited information
online/real-time nonlinear system identification
sequential Kalman filter
unknown/unmeasured input identification
wind loading

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
Mechanics of Materials

Access to Document

10.1002/stc.2919

Cite this

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abstract = "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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N2 - 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.

AB - 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.

KW - input–parameter–state estimation

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KW - online/real-time nonlinear system identification

KW - sequential Kalman filter

KW - unknown/unmeasured input identification

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

Abstract

Data Availability Statement

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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