Vibration Control based on Reinforcement Learning for a Flexible Building-like Structure System with Active Mass Damper against Disturbance Effects

Vibration and displacement control are of critical importance for both high-rise and ultra high-rise building systems. A single-floor building-like structure equipped with an active mass damper (AMD) is investigated in this paper. Optimal vibration control, while dealing with system uncertainties, is realized by the reinforcement learning (RL) technique. When the unexpected natural disasters (such as strong wind excitation) occur, the proposed controller applying to the active mass damper can compensate the increase of the system vibration caused by external disturbances. In addition, a Lyapunov candidate is used to derive a semi-global uniformly ultimately bounded (SGUUB) property. The experimental platform is mainly composed of one flexible floor and a linear cart system. Both the acceleration and the displacement responses of the floor are provided and compared separately for passive mode, proportional-velocity (PV) control and RL control. The experimental results in the form of graphics and tables have shown the effectiveness of the proposed control algorithm.