On Bayesian active vibration control of structures subjected to moving inertial loads

Moradi, Sarvin; Azam, Saeed Eftekhar; Mofid, Massood

doi:10.1016/j.engstruct.2021.112313

Cited by 12 publications

(10 citation statements)

References 61 publications

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“…The parameters of the shear building are taken from De Callafon et al. (2008). The building has eight floors with an inter‐story stiffness of

{10^9}\;{\rm{N}}/{\rm{m}}

and a floor mass of 625 tonnes.…”

Section: Numerical Examplesmentioning

confidence: 99%

“…There is a rapidly growing body of research on the application of Bayesian estimation methods in SHM and structural system identification (Yuen & Mu, 2015;Yuen et al, 2019), model updating and calibration and control (Chatzi & Fuggini, 2015;Chatzi & Smyth, 2009, 2013Chatzis et al, 2017;Corigliano & Mariani, 2004;Erazo et al, 2019;Y. Huang et al, 2020;Lei et al, 2019;Moradi et al, 2021;Ou et al, 2016), and external load estimation (Ebrahimzadeh Hassanabadi et al, 2021;Zhi et al, 2016). Recent advancements in structural response mea-surements have rendered research on big data analytics (e.g., recursive Bayesian estimation) in SHM a subject of great interest.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A linear Bayesian filter for input and state estimation of structural systems

Hassanabadi

Liu

Azam

et al. 2023

Computer aided Civil Eng

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This paper proposes a linear recursive Bayesian filter for minimum variance unbiased joint input and state estimation of structural systems. Unlike the augmented Kalman filter (AKF), the proposed filter falls within the category of Bayesian filters in which unknown inputs are estimated without attributing any fictitious input model or statistics. Also, in contrast with the existing algorithms in the latter category, such as the Gillijns and De Moor Filters (GDFs), the developed filter applies to systems with and without direct feedthrough, in particular, systems with a rank-deficient feedforward matrix. Because of the latter features, the filter is referred to as universal filter (UF) for convenience. The numerical examples show that the UF performs better than the AKF. Due to its structure, the UF does not require the tuning of the hyperparameters for inputs, and therefore the problematic instability of the AKF is not encountered in the case of a large modeling error variance of the input. For systems with direct feedthrough, the error and covariance propagation terms differ due to the distinct state space.Consequently, the UF can enhance estimations due to the well-conditionedness of the relevant inversion problem. Moreover, the UF can deal with systems with rank-deficient feedforward matrix where these systems are not covered by GDFs.

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“…The parameters of the shear building are taken from De Callafon et al. (2008). The building has eight floors with an inter‐story stiffness of

{10^9}\;{\rm{N}}/{\rm{m}}

and a floor mass of 625 tonnes.…”

Section: Numerical Examplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A linear Bayesian filter for input and state estimation of structural systems

Hassanabadi

Liu

Azam

et al. 2023

Computer aided Civil Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…21 Moradi et al proposed a novel Bayesian framework to address measurement errors, modeling uncertainties, and unknown external loads, studying estimations accuracy of three recursive Bayesian methods for virtual sensing. 22 To deal with moving loads with highly non-uniform motions, Wasilewski et al studied an adaptive semi-active controller that relies on a near-optimal switching control law. 23 Stancioiu et al utilized an optimal control method to suppress the vibration of a beam subjected to a moving mass with a constant speed.…”

Section: Introductionmentioning

confidence: 99%

Vibration control of a thin rectangular plate subjected to moving masses using an adaptive sliding mode control method

et al. 2023

Intl J Robust & Nonlinear

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This study investigates the vibration control of a plate subjected to moving masses. The plate model reflects the dynamic behavior more accurately than the beam model which is usually used in the existing literature. However, the vibration control of plates subjected to moving masses is also more challenging as more degrees of freedom need to be taken into account. In addition, the measurement of system status and uncertainty problems in practical application is another nodus. In this paper, combining an adaptive sliding‐mode control (ASMC) strategy and a modal coordinate reconstruction (MCR) method, the MCR‐ASMC method is proposed. The MCR method is employed to capture the plate dynamic behavior for feedback to the ASMC. The ASMC strategy realizes vibration control through the adaptive updating of parameters. Numerical results demonstrate the effectiveness of the proposed method and excellent control performance in vibration suppression of moving mass problems.

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“…14 Simulation results confirmed the effectiveness of the LQG control strategy and its robustness with respect to the uncertainties in the attack angle of the wind loads. Moradi et al 15 proposed an online and real-time Bayesian active control framework for beam-type structures wherein the LQG control framework was combined with an augmented Kalman filter, a dual Kalman filter, and a joint input state estimation algorithm. Numerical simulations were conducted to confirm the validity of this control framework for structures subjected to moving inertial loads at different speeds, and the results were compared with those for a passive control system.…”

Section: Introductionmentioning

confidence: 99%

“…Simulation results confirmed the effectiveness of the LQG control strategy and its robustness with respect to the uncertainties in the attack angle of the wind loads. Moradi et al 15 . proposed an online and real‐time Bayesian active control framework for beam‐type structures wherein the LQG control framework was combined with an augmented Kalman filter, a dual Kalman filter, and a joint input state estimation algorithm.…”

Section: Introductionmentioning

confidence: 99%

A simple strategy for active structural control against control–structure–excitation effects: Concept, framework, and experiments

Yao

Tan

Liu

et al. 2022

Earthq Engng Struct Dyn

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Active control is among the most effective control techniques for mitigating structural vibrations. Control–structure interaction (CSI) plays a significant role in modeling a control system. Popular methods incorporate the actuator dynamics and structure into the control system to account for CSI. The transfer function of the actuator can be identified through frequency response tests without physical modeling. However, this method ignores the effects of the excitation. Another approach is to create a physics‐based model of the actuator and include it in the control design, but its implementation may be challenging and requires a thorough understanding of the complex control system. In this study, the concept of control–structure–excitation effects (CSEE) is presented to account for the effects of the control, structure, and excitation on the transfer function of the actuator. In contrast to the existing control methods that consider CSI in system modeling, an improved control framework is proposed to eliminate the influence of CSI/CSEE on the control performance and thus decouple the tracking control of the actuator from the control design of the structure. This is achieved by introducing the control floor acceleration into the actuator controller and transforming the tracking of the absolute control force into that of the relative acceleration. The control strategy is simple and easy to implement based on conventional actuator control algorithms, such as three‐variable control. Frequency response tests and shake table tests were performed on a two‐story small‐scale structure to evaluate the effectiveness of the control strategy. It was confirmed that the improved control framework shows good tracking performance with respect to the control force and thus allows for ideal structural response control.

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On Bayesian active vibration control of structures subjected to moving inertial loads

Cited by 12 publications

References 61 publications

A linear Bayesian filter for input and state estimation of structural systems

A linear Bayesian filter for input and state estimation of structural systems

Vibration control of a thin rectangular plate subjected to moving masses using an adaptive sliding mode control method

A simple strategy for active structural control against control–structure–excitation effects: Concept, framework, and experiments

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