Application neural network controller and active mass damper in structural vibration suppression

Chen, Chuen-Jyh; Yang, Shih-Ming

doi:10.3233/ifs-141245

Cited by 4 publications

(2 citation statements)

References 35 publications

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“…Most of these studies have been focused on numerical simulation without experimental validation. Recently, Chen and Yang [16] proposed a neural network with modified Newton method for structural control with an AMD and validated the control performance through shake table testing. Experimental results have shown that the neural network controller is effective in damping control applications.…”

Section: Active Mass Dampermentioning

confidence: 99%

Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper

Chen

Chien

2020

Applied Sciences

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In recent years, optimal control which minimizes a cost function formulated by weighted states and control inputs has been applied to the seismic control of structures. Optimal control requires structural states which may not be available in real application; therefore, state estimation is essential, which inevitably takes additional computation time. However, time delay and state estimate error could affect the control performance. In this study, a multilayer perceptron (MLP) model and an autoregressive with exogenous inputs (ARX) model in machine learning are applied to learn the control force generated from a linear-quadratic regulator (LQR) with weighting matrices optimized by applying symbiotic organisms search algorithm. A 10-story building is adopted as a benchmark model for training and validation of the MLP and ARX models. Numerical simulation results demonstrate that the MLP and ARX models are able to emulate the LQR control force from the acceleration response directly, indicating that state estimation is not essential for optimal control implementation in real application. Finally, the machine-learning based approach is experimentally validated by conducting shake table testing in the laboratory in which the structural model is controlled by an active mass damper. The experimental results and structural control performance of the MLP and ARX models are compared with those of the LQR with a Kalman filter.

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Section: Active Mass Dampermentioning

confidence: 99%

Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper

Chen

Chien

2020

Applied Sciences

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“…A filtered sliding mode control method is used for suppressing the vibration of a 76-storey building with an active tuned mass damper in [10]. In [11], on the basis of a modified Newton's method, a multilayer feedforward neural network is presented for suppressing the vibration of a four-storey building structure. In [12], a multi-objective adaptive genetic-fuzzy control strategy is used on an AMD to reduce the wind-induced vibration.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Vibration Control for an Active Mass Damper of a High-Rise Building

Feng

Liu

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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As a kind of large flexible structure, high-rise buildings need to consider wind-resistant and anti-seismic problems for the safety of occupants and properties, especially in coastal areas. This paper proposes an infinite dimensional model and an adaptive boundary control law for an active mass damper(AMD) on this question. The dynamic model of the high-rise building is a combination of some storeys which have flexible walls and rigid floors under a series of physical conditions. Then the adaptive boundary controller is acted on an AMD which is equipped on the top floor, in order to suppress the vibration of every floor and guarantee the comfort of residents. Moreover, simulations and experiments are carried out on a two-floor flexible building to illustrate the effectiveness of the proposed control strategy.

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An integrating genetic algorithm and modified Newton method for tracking control and vibration suppression

Chen

2019

Artif Intell Rev

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Application neural network controller and active mass damper in structural vibration suppression

Cited by 4 publications

References 35 publications

Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper

Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper

Adaptive Vibration Control for an Active Mass Damper of a High-Rise Building

An integrating genetic algorithm and modified Newton method for tracking control and vibration suppression

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