Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker

Force identification in structural dynamics is an inverse problem concerned with finding loads from measured structural response. The main objective of this thesis is to perform and study state (displacement and velocity) and force estimation by Kalman filtering.Theory on optimal control and state-s...

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Main Author: Petersen, Øyvind Wiig
Other Authors: Øiseth, Ole Andre, Nord, Torodd Skjerve, Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for konstruksjonsteknikk
Format: Master Thesis
Language:English
Published: Institutt for konstruksjonsteknikk 2014
Subjects:
Online Access:http://hdl.handle.net/11250/237547
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spelling ftntnutrondheimi:oai:ntnuopen.ntnu.no:11250/237547 2023-05-15T18:18:41+02:00 Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker Petersen, Øyvind Wiig Øiseth, Ole Andre Nord, Torodd Skjerve Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for konstruksjonsteknikk 2014 http://hdl.handle.net/11250/237547 eng eng Institutt for konstruksjonsteknikk 749691 ntnudaim:11302 http://hdl.handle.net/11250/237547 110 Master thesis 2014 ftntnutrondheimi 2019-09-17T06:48:47Z Force identification in structural dynamics is an inverse problem concerned with finding loads from measured structural response. The main objective of this thesis is to perform and study state (displacement and velocity) and force estimation by Kalman filtering.Theory on optimal control and state-space models are presented, adapted to linear structural dynamics. Accommodation for measurement noise and model inaccuracies are attained by stochastic-deterministic coupling. Explicit requirements for discrete time-invariant steady filter convergence are derived. From a finite element model and measurement data, unbiased estimation of state and force history is performed by an augmented Kalman filter, based on minimizing error variance.A numerical example on a system with two degrees of freedom displays adequate filtering capabilities. Experimental validation is performed on a simply supported beam instrumented with accelerometer and three strain gauges. The studies demonstrate successful identification of impact forces with both collocated and non-collocated sensors. The corresponding state estimation displays good accuracy. Limiting the number of measurements is tested. The minimal observation setup (one accelerometer and one strain gauge) is analytically stable, but results are found to be significantly deteriorated, even with collocation. Moreover the influence of model errors is investigated, imposed as random contributions in mass and stiffness matrices. The estimation of impact forces and states show fair robustness against moderate mass and stiffness deviations.A short case study on the offshore Hanko channel marker is presented, exposed to moving sea ice. A finite element model is created, instrumented with a tiltmeter and three accelerometers. Numerical simulations show identification of ice forces is viable, but heavily relies on the model representation accuracy. Master Thesis Sea ice NTNU Open Archive (Norwegian University of Science and Technology)
institution Open Polar
collection NTNU Open Archive (Norwegian University of Science and Technology)
op_collection_id ftntnutrondheimi
language English
description Force identification in structural dynamics is an inverse problem concerned with finding loads from measured structural response. The main objective of this thesis is to perform and study state (displacement and velocity) and force estimation by Kalman filtering.Theory on optimal control and state-space models are presented, adapted to linear structural dynamics. Accommodation for measurement noise and model inaccuracies are attained by stochastic-deterministic coupling. Explicit requirements for discrete time-invariant steady filter convergence are derived. From a finite element model and measurement data, unbiased estimation of state and force history is performed by an augmented Kalman filter, based on minimizing error variance.A numerical example on a system with two degrees of freedom displays adequate filtering capabilities. Experimental validation is performed on a simply supported beam instrumented with accelerometer and three strain gauges. The studies demonstrate successful identification of impact forces with both collocated and non-collocated sensors. The corresponding state estimation displays good accuracy. Limiting the number of measurements is tested. The minimal observation setup (one accelerometer and one strain gauge) is analytically stable, but results are found to be significantly deteriorated, even with collocation. Moreover the influence of model errors is investigated, imposed as random contributions in mass and stiffness matrices. The estimation of impact forces and states show fair robustness against moderate mass and stiffness deviations.A short case study on the offshore Hanko channel marker is presented, exposed to moving sea ice. A finite element model is created, instrumented with a tiltmeter and three accelerometers. Numerical simulations show identification of ice forces is viable, but heavily relies on the model representation accuracy.
author2 Øiseth, Ole Andre
Nord, Torodd Skjerve
Norges teknisk-naturvitenskapelige universitet, Fakultet for ingeniørvitenskap og teknologi, Institutt for konstruksjonsteknikk
format Master Thesis
author Petersen, Øyvind Wiig
spellingShingle Petersen, Øyvind Wiig
Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
author_facet Petersen, Øyvind Wiig
author_sort Petersen, Øyvind Wiig
title Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
title_short Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
title_full Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
title_fullStr Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
title_full_unstemmed Model-based stochastic-deterministic State and Force Estimation using Kalman filtering with Application to Hanko-1 Channel Marker
title_sort model-based stochastic-deterministic state and force estimation using kalman filtering with application to hanko-1 channel marker
publisher Institutt for konstruksjonsteknikk
publishDate 2014
url http://hdl.handle.net/11250/237547
genre Sea ice
genre_facet Sea ice
op_source 110
op_relation 749691
ntnudaim:11302
http://hdl.handle.net/11250/237547
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