Deep reinforcement learning-based active mass driver decoupled control framework considering control–structure interaction effects

Abstract

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.