Rapid Controller Generation for Vibration Suppression of Structures Using Direct Excitation with Machine Learning
Publication: Journal of Structural Engineering
Volume 150, Issue 3
Abstract
Active mass dampers (AMDs) have been used to suppress vibration of structures subjected to dynamic loading. Generally, controller design of AMD requires an identified numerical model of the structure. However, unmodeled dynamics and system uncertainties could lead to mediocre control performance. In this study, a novel controller synthesis method of AMD named direct excitation with machine learning (DEML) is proposed and verified. In DEML, the AMD on top of the structure generates trivial excitation with sufficient bandwidth. Then the corresponding structural acceleration response is collected and used for training a controller formulated in an artificial neural network. Accordingly, the associated controller can be realized and implemented to generate control force from the structural acceleration response directly. Seismic control performance of the controller synthesized by DEML was verified numerically in which 9-story and 27-story building models were considered. Moreover, shake table testing of an AMD on top of a 3-story structural specimen was conducted to further validate the effectiveness of the proposed DEML. Experimental results demonstrate that the controller synthesized by DEML achieves competitive seismic control performance compared with a conventional linear-quadratic Gaussian controller.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The study was supported by the Taiwan Building Technology Center (TBTC) at National Taiwan University of Science and Technology from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan. In addition, the experiments were conducted in the small-scale structural laboratory of the National Center for Research on Earthquake Engineering (NCREE) in Taiwan. The authors are grateful to the financial support of TBTC as well as the usage of experimental facilities and the technical support from the technicians of NCREE.
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Information & Authors
Information
Published In
Journal of Structural Engineering
Volume 150 • Issue 3 • March 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 19, 2023
Accepted: Nov 2, 2023
Published online: Dec 27, 2023
Published in print: Mar 1, 2024
Discussion open until: May 27, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Continuum mechanics
- Damping
- Dynamic loads
- Dynamics (solid mechanics)
- Earthquake engineering
- Engineering fundamentals
- Engineering mechanics
- Excitation (physics)
- Geotechnical engineering
- Models (by type)
- Motion (dynamics)
- Numerical models
- Seismic effects
- Seismic tests
- Solid mechanics
- Structural dynamics
- Tests (by type)
- Vibration
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