Exploiting input sparsity for joint state/input moving horizon estimation

Kirchner, Matteo; Croes, J. James R.; Cosco, Francesco; Desmet, Wim

doi:10.1016/j.ymssp.2017.08.024

Cited by 18 publications

(6 citation statements)

References 44 publications

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“…The real and imaginary parts of the force acting on location j are selected with L j , and the slack variables s and t j are selected with g and c j , respectively. The vectors f ∈ R 2N np+np+1 and b ∈ R 2N n d are defined as: 10) where the matrix 1 m×n ∈ R m×n contains ones.…”

Section: Discussionmentioning

confidence: 99%

“…Qiao et al [9], however, proposed the use of interior point methods to solve this regularized problem. Kirchner et al [10] recently developed the compressive sensing-moving horizon estimator (CS-MHE) which combines a moving horizon estimator (MHE) with an ℓ 1 regularization term. This allows for a recursive solution of the force localization problem in the time domain.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Localization of dynamic forces on structures with an interior point method using group sparsity

Wambacq

Maes

Rezayat

et al. 2019

Mechanical Systems and Signal Processing

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This paper presents an algorithm for the localization of forces applied to a structure. The force estimation is performed in the frequency domain based on a limited number of sensors and a linear dynamic system model and it involves minimizing an objective function penalized with a group sparsity term. The minimization of this objective function is formulated as a second order cone program, which is solved using an interior point method. This allows for a reduction of the calculation time when compared to other algorithms that are currently available to enforce group sparsity on the forces, especially for large scale problems. The presented algorithm is first verified using numerical simulations. Next, a validation is performed using data obtained from a field test on a footbridge, where two locations on the bridge deck are excited using hammer impacts and the force localization is performed assuming a total of 108 possible force locations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Localization of dynamic forces on structures with an interior point method using group sparsity

Wambacq

Maes

Rezayat

et al. 2019

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…This approach is chosen as in general no prior information on the input is assumed to be known. However, if useful information is available, e.g., periodicity of the input, other input models can be employed, as in [ 24 ].…”

Section: State-input Estimation For Mb Modelsmentioning

confidence: 99%

A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

Adduci

Vermaut

Naets

et al. 2021

Sensors

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Model-based force estimation is an emerging methodology in the mechatronic community given the possibility to exploit physically inspired high-fidelity models in tandem with ready-to-use cheap sensors. In this work, an inverse input load identification methodology is presented combining high-fidelity multibody models with a Kalman filter-based estimator and providing the means for an accurate and computationally efficient state-input estimation strategy. A particular challenge addressed in this work is the handling of the redundant state-description encountered in common multibody model descriptions. A novel linearization framework is proposed on the time-discretized equations in order to extract the required system model matrices for the Kalman filter. The presented framework is experimentally validated on a slider-crank mechanism. The nonlinear kinematics and dynamics are well represented through a rigid multibody model with lumped flexibilities to account for localized interaction phenomena among bodies. The proposed methodology is validated estimating the input torque delivered by a driver electro-motor together with the system states and comparing the experimental data with the estimated quantities. The results show the stability and accuracy of the estimation framework by only employing the angular motor velocity, measured by the motor encoder sensor and available in most of the commercial electro-motors.

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“…In pursuit of this goal, the objective of this paper is to implement a Gaussian process latent force model which improves upon existing methods by reducing the dependency on manual tuning of covariance matrices associated with the unknown inputs and also provides numerical stability with respect to observability of the augmented state-space formulation when used with only acceleration measurements. The location of the inputs is assumed to be known, however studies on input localization can be found in [26,27,28].…”

Section: Objectivementioning

confidence: 99%

A Gaussian process latent force model for joint input-state estimation in linear structural systems

Nayek

Chakraborty

Narasimhan

2019

Mechanical Systems and Signal Processing

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The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force models (GPLFMs) are hybrid models that combine differential equations representing a physical system with data-driven non-parametric Gaussian process models. In this work, the unknown input forces acting on a structure are modelled as Gaussian processes with some chosen covariance functions which are combined with the mechanistic differential equation representing the structure to construct a GPLFM. The GPLFM is then conveniently formulated as an augmented stochastic state-space model with additional states representing the latent force components, and the joint input and state inference of the resulting model is implemented using Kalman filter. The augmented state-space model of GPLFM is shown as a generalization of the class of input-augmented state-space models, is proven observable, and is robust compared to conventional augmented formulations in terms of numerical stability. The hyperparameters governing the covariance functions are estimated using maximum likelihood optimization based on the observed data, thus overcoming the need for manual tuning of the hyperparameters by trial-and-error. To assess the performance of the proposed GPLFM method, several cases of state and input estimation are demonstrated using numerical simulations on a 10-dof shear building and a 76-storey ASCE benchmark office tower. Results obtained indicate the superior performance of the proposed approach over conventional Kalman filter based approaches.

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Exploiting input sparsity for joint state/input moving horizon estimation

Cited by 18 publications

References 44 publications

Localization of dynamic forces on structures with an interior point method using group sparsity

Localization of dynamic forces on structures with an interior point method using group sparsity

A Discrete-Time Extended Kalman Filter Approach Tailored for Multibody Models: State-Input Estimation

A Gaussian process latent force model for joint input-state estimation in linear structural systems

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