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Importance-weighted actor-learner framework for reinforcement learning vibration control of three coupled flexible beams.

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Title: Importance-weighted actor-learner framework for reinforcement learning vibration control of three coupled flexible beams.
Authors: Liu, Hao-ran1 (AUTHOR), Qiu, Zhi-cheng1 (AUTHOR) zhchqiu@scut.edu.cn
Source: Journal of Vibration & Control. Aug2026, Vol. 32 Issue 15/16, p4118-4130. 13p.
Abstract: Three-coupled Flexible Beam (TCFB) system is a kind of vibration structure characterized by low damping, close space modes, and coupled vibrations. To address these challenges, the control scheme of reinforcement learning (RL) algorithm based on Importance-weights Actor-Learner Architecture (IMPALA) is applied. Using wavelet transform and Peafowl Optimization Algorithm (POA) to identify the system model matrices, an accurate system model is obtained, used to train RL algorithm. After training, the effectiveness of the IMPALA controller is verified by experiments. Compared with the traditional large gain proportional-derivative (PD) controllers, the experimental results show that the IMPALA controller can enhance vibration suppression, especially in rapidly attenuating small amplitude vibration. The IMPALA controller shows excellent performance and proves robustness. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 195315227
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PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
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  Data: Importance-weighted actor-learner framework for reinforcement learning vibration control of three coupled flexible beams.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Hao-ran%22">Liu, Hao-ran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Zhi-cheng%22">Qiu, Zhi-cheng</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhchqiu@scut.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Vibration+%26+Control%22">Journal of Vibration & Control</searchLink>. Aug2026, Vol. 32 Issue 15/16, p4118-4130. 13p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Three-coupled Flexible Beam (TCFB) system is a kind of vibration structure characterized by low damping, close space modes, and coupled vibrations. To address these challenges, the control scheme of reinforcement learning (RL) algorithm based on Importance-weights Actor-Learner Architecture (IMPALA) is applied. Using wavelet transform and Peafowl Optimization Algorithm (POA) to identify the system model matrices, an accurate system model is obtained, used to train RL algorithm. After training, the effectiveness of the IMPALA controller is verified by experiments. Compared with the traditional large gain proportional-derivative (PD) controllers, the experimental results show that the IMPALA controller can enhance vibration suppression, especially in rapidly attenuating small amplitude vibration. The IMPALA controller shows excellent performance and proves robustness. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Vibration & Control is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1177/10775463251351680
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 4118
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      – TitleFull: Importance-weighted actor-learner framework for reinforcement learning vibration control of three coupled flexible beams.
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            NameFull: Liu, Hao-ran
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            NameFull: Qiu, Zhi-cheng
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            – D: 01
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
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