Experimental investigation on dynamic characterization and seismic control performance of a TLPD system

2016 ◽  
Vol 26 (7) ◽  
pp. e1350 ◽  
Author(s):  
Kaoshan Dai ◽  
Jianze Wang ◽  
Rifeng Mao ◽  
Zheng Lu ◽  
Shen-En Chen
2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Y. Y. Chen ◽  
W. Zhao ◽  
C. Y. Shen ◽  
Z. C. Qian

Nonlinear energy sink (NES) has proven to be very effective in reducing the vibration response of structures. In this paper, a magnetic bistable nonlinear energy sink (BNES) that composed of a guided moving mass attached with linear springs and permanent magnets is proposed. To assess the seismic control performance of the proposed BNES, a shear frame model equipped with the proposed BNES is compared with the same shear frame model equipped with an optimized cubic NES and with a linear tuned mass damper (TMD) system. The results show that, in the idealized situation, where the mass and stiffness is clearly defined (no uncertainty), the BNES can achieve similar performance as a thoroughly in-tuned TMD system. Moreover, in the detuned condition, due to broadband high internal resonance capability, the proposed BNES can outperform the linear TMD and the cubic NES. The study demonstrates that the proposed BNES can be used as an efficient passive vibration absorber for structural seismic control.


Author(s):  
Lauren Mazella ◽  
Chaitanya C. Paruchuri ◽  
Giovanni Lacagnina ◽  
Yijun Mao ◽  
Phillip Joseph

2020 ◽  
Vol 10 (15) ◽  
pp. 5342
Author(s):  
Pei-Ching Chen ◽  
Kai-Yi Chien

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.


2017 ◽  
Vol 411 ◽  
pp. 362-377 ◽  
Author(s):  
Qichao Xue ◽  
Jingcai Zhang ◽  
Jian He ◽  
Chunwei Zhang ◽  
Guangping Zou

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