基于深度强化学习的拉索智能减振算法

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振动与冲击 ›› 2022, Vol. 41 ›› Issue (23) : 175-181.

论文

基于深度强化学习的拉索智能减振算法

陈孝聪1，张恩启1，程斌1，王浩2

作者信息 +

Intelligent vibration reduction algorithm of cable based on deep reinforcement learning

CHEN Xiaocong1, ZHANG Enqi1, CHENG Bin1, WANG Hao2

Author information +

文章历史 +

摘要

为了降低振动控制算法对拉索-MR阻尼器系统动力学模型精度的依赖性，提出一种基于深度强化学习理论的无模型减振算法。该方法利用控制模块与环境之间的交互实现对拉索振动的自适应半主动控制，依据拉索特定点响应状态在线调节阻尼器施加电压，降低反馈控制要求。为验证智能控制算法的有效性，采用Galerkin法建立拉索-MR阻尼器环境模型，并以实桥拉索减振设计为例对比分析了粘滞阻尼器多模态控制、Bang-Bang控制、深度强化学习控制对拉索的风振控制效果。结果表明：在随机风荷载作用下，深度强化学习控制算法不仅能够实现对拉索的无模型振动控制，且控制效果优于粘滞阻尼器多模态控制和MR阻尼器的Bang-Bang控制。
关键词：拉索；振动控制；深度强化学习；MR阻尼器；风致振动

Abstract

In order to reduce the dependence on the accuracy of cable-MR damper system’s dynamic model, a model-free vibration control algorithm was proposed based on deep reinforcement learning theories. The algorithm utilized the interaction between the control module and environment model to control vibrations adaptively. The input voltage of MR damper was adjusted online according to the responses at specific location of the cable, which reduced feedback requirements. In order to verify the effectiveness of proposed intelligent control algorithm, Galerkin method was used to establish the environment simulation model, and comparative analysis of an actual stay cable vibration responses under optimal viscous damper multi-mode vibration control, Bang-Bang control and deep reinforcement learning control strategies was conducted. The results show that the deep reinforcement learning control algorithm can achieve model-free vibration control of the stay cable, and its control efficiency is better than the optimal passive control of viscous damper and Bang-Bang control of MR damper.
Key words: cable; vibration control; deep reinforcement learning; MR damper; wind-induced vibration

导出引用

陈孝聪1，张恩启1，程斌1，王浩2. 基于深度强化学习的拉索智能减振算法[J]. 振动与冲击, 2022, 41(23): 175-181

CHEN Xiaocong1, ZHANG Enqi1, CHENG Bin1, WANG Hao2. Intelligent vibration reduction algorithm of cable based on deep reinforcement learning[J]. Journal of Vibration and Shock, 2022, 41(23): 175-181

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