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
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. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(23): 175-181.
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