Control–structure interaction (CSI) plays a significant role in active control systems. Popular methods incorporate actuator dynamics into an integrated control system to account for CSI, leading to a situation where existing structural control algorithms that ignore CSI cannot be applied directly. To address this issue, this study proposes a deep reinforcement learning (DRL) based active mass driver (AMD) decoupled control framework, in which a structural control algorithm is employed to generate the control force command without consideration of CSI, while a DRL agent is utilized to attenuate the CSI effects of AMD systems and achieve a desired control force. The DRL-based AMD control framework is verified through a series of numerical experiments. Furthermore, the applicability of the control framework is confirmed in a wind-excited 76-story benchmark building. Comprehensive analysis indicates that the proposed control framework can effectively eliminate the CSI effects and apply accurate control force to the structure in various scenarios, which allows for an ideal structural response control.

中文翻译：

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考虑控制-结构相互作用效应的基于深度强化学习的主动质量驱动器解耦控制框架

控制结构交互（CSI）在主动控制系统中发挥着重要作用。流行的方法将执行器动力学纳入集成控制系统以考虑 CSI，导致现有忽略 CSI 的结构控制算法无法直接应用。为了解决这个问题，本研究提出了一种基于深度强化学习（DRL）的主动质量驱动器（AMD）解耦控制框架，其中采用结构控制算法来生成控制力命令而不考虑CSI，而DRL代理是用于减弱 AMD 系统的 CSI 效应并实现所需的控制力。基于DRL的AMD控制框架通过一系列数值实验得到验证。此外，该控制框架的适用性在76层风励标杆建筑中得到了证实。综合分析表明，所提出的控制框架可以有效消除CSI效应，并在各种情况下对结构施加精确的控制力，从而实现理想的结构响应控制。