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All Journal EPI International Journal of Engineering
Shinya Honda
Faculty of Engineering, Hokkaido University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Adaptive Vibration Control of Smart Structure Using Deep Reinforcement Learning Shinya Honda; Yuta Imura; Katsuhiko Sasaki; Ryo Takeda
EPI International Journal of Engineering Vol 5 No 2 (2022): Volume 5 Number 2, August 2022
Publisher : Center of Techonolgy (COT), Engineering Faculty, Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25042/epi-ije.082022.03

Abstract

In this research, the authors developed an adaptive control method using deep reinforcement learning which is a kind of machine learning to suppress the vibration of smart structures. This method just requires information about the control response and input, and don’t require numerical models for the controlled object to design the controller. We experimented to verify the effectiveness of this method. In this experiment, a smart structure fabricated by an aluminum plate and a piezoelectric actuator was used as a controlled object. Three kinds of reinforcement learning algorithms are employed, Deep Q Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), and the control performance is compared. As a result, we succeeded in reducing the norm of the frequency response to impulse disturbance by up to about 40 dB compared to the uncontrolled case. This demonstrates the applicability of the control method using deep reinforcement learning to adaptive vibration control.
Co-Authors Katsuhiko Sasaki Ryo Takeda Yuta Imura