Application of the unscented Kalman filter for real-time nonlinear structural system identification

Wu, Meiliang; Smyth, Andrew W.

doi:10.1002/stc.186

Cited by 255 publications

(138 citation statements)

References 34 publications

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“…Whereas EKF uses the differentiation of nonlinear dynamical and observation operators to evaluate the propagation of probability moments, UKF instead employs well-chosen sampling points which are propagated through the nonlinear operators themselves to evaluate the resulting empirical moments. This eliminates the need for tangent operator implementations, and it can be argued that propagated moments are then more accurately approximated [12], see also [23] for a comparison of UKF and EKF in an example of parametric estimation in a mechanical system. However, UKF seems to have been -so far -restricted to systems of relatively limited sizes due to the lack of reduced-order versions for this approach, as commented on in [22].…”

Section: Introductionmentioning

confidence: 99%

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Moireau¹,

Chapelle²

2010

ESAIM: COCV

103

155

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Abstract.We propose a general reduced-order filtering strategy adapted to Unscented Kalman Filtering for any choice of sampling points distribution. This provides tractable filtering algorithms which can be used with large-dimensional systems when the uncertainty space is of reduced size, and these algorithms only invoke the original dynamical and observation operators, namely, they do not require tangent operator computations, which of course is of considerable benefit when nonlinear operators are considered. The algorithms are derived in discrete time as in the classical UKF formalismwell-adapted to time discretized dynamical equations -and then extended into consistent continuoustime versions. This reduced-order filtering approach can be used in particular for the estimation of parameters in large dynamical systems arising from the discretization of partial differential equations, when state estimation can be handled by an adequate Luenberger observer inspired from feedback control. In this case, we give an analysis of the joint state-parameter estimation procedure based on linearized error, and we illustrate the effectiveness of the approach using a test problem inspired from cardiac biomechanics.Mathematics Subject Classification. 93E11, 93B30, 35R30, 74H15.

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Section: Introductionmentioning

confidence: 99%

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Moireau¹,

Chapelle²

2010

ESAIM: COCV

103

155

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“…Other variants could also be constructed by considering for parameter estimation other types of nonlinear filtering than EKF-such as unscented Kalman filtering [20,40] -or recursive identification algorithms, see e.g. [29].…”

Section: Discussionmentioning

confidence: 99%

Joint state and parameter estimation for distributed mechanical systems

Moireau

Chapelle

Tallec

2008

Computer Methods in Applied Mechanics and Engineering

102

149

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We present a novel strategy to perform estimation for a dynamical mechanical system in standard operating conditions, namely, without ad hoc experimental testing. We adopt a sequential approach, and the joint state-parameter estimation procedure is based on a state estimator inspired from collocated feedback control. This type of state estimator is chosen due to its particular effectiveness and robustness, but the methodology proposed to adequately extend state estimation to joint stateparameter estimation is general, and -indeed -applicable with any other choice of state feedback observer. The convergence of the resulting joint estimator is mathematically established. In addition, we demonstrate its effectiveness with a biomechanical test problem defined to feature the same essential characteristics as a heart model, in which we identify localized contractility and stiffness parameters using measurements of a type that is available in medical imaging.

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“…The main objective of this study is to establish a computational framework for identifying and adjusting these parameters, while estimating the structural states, in a problem that is referred to as joint state and parameter estimation (JS&PE) [6][7]. To achieve this, the Unscented Kalman Filter (UKF) is utilized [8], due to its efficient performance of in real-time nonlinear system identification problems [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Model Updating of a Nonlinear Experimental Vehicle Using Substructuring and Unscented Kalman Filtering

Tsotalou¹,

Giagopoulos²,

Dertimanis³

et al. 2017

Proceedings of the 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering (UNCECO

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Application of the unscented Kalman filter for real-time nonlinear structural system identification

Cited by 255 publications

References 34 publications

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Reduced-order Unscented Kalman Filtering with application to parameter identification in large-dimensional systems

Joint state and parameter estimation for distributed mechanical systems

Model Updating of a Nonlinear Experimental Vehicle Using Substructuring and Unscented Kalman Filtering

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