Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

Huang, Jinshan; Li, Xianzhi; Yang, Xiongjun; Zheng, Zhuo; Lei, Ying

doi:10.1177/1461348419868860

Cited by 8 publications

(6 citation statements)

References 26 publications

(27 reference statements)

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“…In general, when only acceleration data is used, the joint input-state estimation based on the Kalman filter is prone to the force drift problem [42]. Some researchers solved this problem by introducing partial displacement or strain data [27][28][29], however, it may not be a cost-effective solution. In the proposed method, the PM algorithm is used to solve the force drift problem without additional measurement data, which is achieved by applying sparse constraints on the force vectors.…”

Section: Input Estimationmentioning

confidence: 99%

“…The force drift problem means that the low-frequency component of the estimated force deviates from the true steady-state position, while the high-frequency information of the estimated force can be relatively accurate. Some researchers have proposed that partial displacement or strain data [26][27][28][29] could be used to solve this problem. However, this may not be a cost-effective solution.…”

Section: Introductionmentioning

confidence: 99%

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Structural damage identification with unknown external inputs based on the sparse constraint

Wei

Cai

et al. 2023

Smart Mater. Struct.

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The strength and integrity of a structure depends not only on the quality of its materials but also the health condition of its different components. In some cases, damage cracks inside the structural will be generated from excessive loads, and if these damage cracks go undetected for a long period, catastrophic failure of structures may occur. However, most of the damages at early stages are minor and difficult to detect by visual inspection. Therefore, this paper proposes a damage estimation algorithm based on unscented Kalman filter (UKF), which can identify and locate the damage parameters in real time with a limited number of sensors. Meanwhile, this algorithm is also applicable to the structural system with unknown external inputs, and can make the joint force-damage estimation. For most structures, the damage parameters are sparse in space, therefore, the sparsity of the damage parameter vector is introduced to UKF as an l1-norm constraint by the pseudo- measurement (PM) technique. Thus, unconstrained optimization of the damage parameter estimation is transformed into an l1-norm constrained optimization problem. With such improvement, the process of damage parameter estimation converges faster and the false damage parameters are effectively restrained. Moreover, in order to solve the force drift problem during force identification when only acceleration data is used, the sparse constraint of the force vector is also introduced to UKF framework by the PM technique. Finally, numerical simulations of a ten-story shear building and experiments of a three-story shear structure are used to demonstrate the performance of the proposed algorithm. The results indicate that this algorithm can achieve an accurate estimation of damage and can successfully resolve the common force drift problem.

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Section: Input Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Structural damage identification with unknown external inputs based on the sparse constraint

Wei

Cai

et al. 2023

Smart Mater. Struct.

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“…In this study, the acceleration response at the locations of the unknown excitation loadings is unknown; therefore,

D_{(|, k + 1) k}^{u} = 0

in Equation (16), which means the measurement/observation equation does not contain the unknown excitation loading

f_{k + 1}^{u}

. Currently, most of the existing EKF‐UI approaches 32,33 require the existence of unknown forces in the measurement equation; that is,

D_{(|, k + 1) k}^{u}

stands for a full‐rank matrix. In this paper, an UGEKF‐UI is developed to circumvent the limitations of the existing EKF‐UI methods.…”

Section: Nonparametric Identification With Partial Output Excluding A...mentioning

confidence: 99%

“…Liu et al 32 presented a data fusion‐based EKF with unknown inputs (EKF‐UI) approach for simultaneous identification of structural parameters and unknown excitations. Based on the method proposed by Liu et al, an EKF‐UI algorithm combined with data fusion using both acceleration and strain measurements was proposed by Huang et al 33 and a five‐story shear building model was used to verify the feasibility of the proposed method. By introducing a projection matrix in the observation equation, a time‐domain EKF‐based approach was proposed by He et al 34 for simultaneous identification of structural parameters and the unknown excitations with limited outputs, where unknown input was identified using a least‐squares estimation approach.…”

Section: Introductionmentioning

confidence: 99%

Hysteresis and dynamic loading nonparametric identification for multi‐degree‐of‐freedom structures using an updated general extended Kalman filter and a Legendre polynomial model

Zhao

Deng

et al. 2022

Structural Contr & Hlth

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In order to identify the hysteretic behavior in the form of nonlinear restoring force (NRF) and the unknown dynamic excitation when the acceleration measurement at the degree of freedom (DOF) of the excitation is unknown, and considering the fact that it is difficult to establish a general parametric mathematical model in advance to describe the real hysteretic behavior of an engineering structure, in this paper, a nonparametric identification approach for both NRF and dynamic loading is presented using an updated general extended Kalman filter with unknown input (UGEKF-UI) algorithm with lim-

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“…Based on the identification algorithm in Stage I, the identified structural parameters are shown and compared to the exact parameters in Table 5. The exact values of stiffness and damping parameters are obtained by experiments referred from the reference 47 …”

Section: Experimental Validationmentioning

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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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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Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

Cited by 8 publications

References 26 publications

Structural damage identification with unknown external inputs based on the sparse constraint

Structural damage identification with unknown external inputs based on the sparse constraint

Hysteresis and dynamic loading nonparametric identification for multi‐degree‐of‐freedom structures using an updated general extended Kalman filter and a Legendre polynomial model

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

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