A general synthesis of identification and vibration control of building structures under unknown excitations

Lei, Ying; Lu, Jubin; Huang, Jinshan; Chen, Siyi

doi:10.1016/j.ymssp.2020.106803

Cited by 25 publications

(14 citation statements)

References 37 publications

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“…Many existing studies in the broader literature [76,[95][96][97][98][99][100][101] have successfully implemented adaptive control schemes on structures. Some controllers, such as the Simple Adaptive Controller (SAC), recently studied by Soares et al [18,102], can be used to reduce the seismic response of structures under seismic excitations.…”

Section: Introductionmentioning

confidence: 99%

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Sabetahd

Ghasemi

Poursorkhabi

et al. 2022

Computational Intelligence and Neuroscience

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The present study aims to design a robust adaptive controller employed in the active tuned mass damper (ATMD) system to overcome undesirable vibrations in multistory buildings under seismic excitations. We propose a novel adaptive type-2 neural-fuzzy controller (AT2NF). All system parameters are taken as unknowns. The MLP neural network is used to extract the Jacobian and estimate the structural model; then, the estimated model is applied to the controller online. To tune the control force applied to the ATMD and achieve the control targets, the controller parameters are adaptively trained using the extended Kalman Filter (EKF) and the error back-propagation algorithm. A PID controller is also included in this method to increase the stability and robustness of the adaptive type-2 neural-fuzzy controller against seismic vibrations. An online simple adaptive controller (OSAC) is studied to demonstrate the suggested controller’s superiority. The OSAC is based on adaptive control of the implicit reference model. In this proposed method, the EKF is used to tune the controller parameters online as a novel feature. The uncertainty associated with identifying the mechanical properties of structures, such as mass and stiffness, is one of the primary challenges in the real-time control of structures. This paper investigates how both controllers cope with parametric uncertainties under far-field and near-field seismic excitation. According to numerical results, the AT2NF controller outperforms OSAC in minimizing the dynamic responses of the structure during an earthquake and accomplishing control objectives when the structure’s characteristics change.

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Section: Introductionmentioning

confidence: 99%

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Sabetahd

Ghasemi

Poursorkhabi

et al. 2022

Computational Intelligence and Neuroscience

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“…Field measurements of vibration and noise on the ground and inside a nearby 3-story building subjected to moving subway trains were conducted by Zou et al [2]. A general synthesis of identification and vibration control of building structures under unknown excitations was proposed by Lei et al [3], which has been proven to be cost-effective and beneficial for developing smart building structures.…”

Section: Introductionmentioning

confidence: 99%

Improved Empirical Modal Decomposition Coupled with Interwoven Fourier Decomposition for Building Vibration Signal Denoising

Luo

Abubakar

2022

Shock and Vibration

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The precise detection of building vibration signals is a crucial problem for the identification of building vibration sources and characteristics. However, the building vibration signal is usually accompanied by complex high-frequency noise. The present study proposed a novel building vibration signal denoising method based on improved empirical modal decomposition coupled with interwoven Fourier decomposition (IEMD-IWFD). The noise-embed building vibration signal is first decomposed by the IEMD-IWFD. Then, the intrinsic mode function (IMF) components with useful information are extracted from the original building vibration signal using the energy criterion of the autocorrelation function. After that, the building vibration signal is formed by reconstructing the IMF component using the Hilbert transform. Based on the comparison of similarity coefficient and mean square error between the reconstructed signal from IEMD-IWFDM and EMD and target signal, it is indicated that the IEMD-IWFDM exhibits a better denoising performance for the simulated building vibration signal induced by trains.

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“…To address this limitation, Pan et al 36 put forward a general extended Kalman filter with unknown input (GEKF-UI) to simultaneously estimate the structural parameters and the unknown excitations for a linear structural system. Most recently, to synthesize structural identification with vibration control, Lei et al 37,38 employed the GEKF-UI method to identify structural systems and unknown excitation for time-varying and time-invariant structural systems.…”

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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A general synthesis of identification and vibration control of building structures under unknown excitations

Cited by 25 publications

References 37 publications

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Response Attenuation of a Structure Equipped with ATMD under Seismic Excitations Using Methods of Online Simple Adaptive Controller and Online Adaptive Type-2 Neural-Fuzzy Controller

Improved Empirical Modal Decomposition Coupled with Interwoven Fourier Decomposition for Building Vibration Signal Denoising

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

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