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Article

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

Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei City 10607, Taiwan
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Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(15), 5342; https://doi.org/10.3390/app10155342
Received: 28 June 2020 / Revised: 19 July 2020 / Accepted: 23 July 2020 / Published: 3 August 2020
(This article belongs to the Special Issue Seismic Structural Health Monitoring)
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. View Full-Text
Keywords: machine learning; multilayer perceptron; autoregressive neural network; optimal control; active mass damper; seismic performance; shake table testing machine learning; multilayer perceptron; autoregressive neural network; optimal control; active mass damper; seismic performance; shake table testing
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MDPI and ACS Style

Chen, P.-C.; Chien, K.-Y. Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper. Appl. Sci. 2020, 10, 5342. https://doi.org/10.3390/app10155342

AMA Style

Chen P-C, Chien K-Y. Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper. Applied Sciences. 2020; 10(15):5342. https://doi.org/10.3390/app10155342

Chicago/Turabian Style

Chen, Pei-Ching, and Kai-Yi Chien. 2020. "Machine-Learning Based Optimal Seismic Control of Structure with Active Mass Damper" Applied Sciences 10, no. 15: 5342. https://doi.org/10.3390/app10155342

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