Reinforcement learning‐based control to suppress the transient vibration of semi‐active structures subjected to unknown harmonic excitation

Abstract

The problem of adaptive semi-active control of transient structural vibration induced by unknown harmonic excitation is studied. The controller adaptation is attained by using a specially designed reinforcement learning algorithm that adjusts the parameters of a switching control policy to guarantee efficient dissipation of the structural energy. This algorithm relies on an efficient gradient-based sequence that accelerates the learning protocol and results in suboptimal control. The performance of this method is examined through numerical experiments for a span structure that is equipped with a semi-active device of controlled stiffness and damping parameters.The experiments cover a selection of control learning scenarios and comparisons to optimal open-loop and heuristic state-feedback control strategies. This study has confirmed that the developed method has high stabilizing performance, and the relatively low computational burden of the incorporated iterative learning algorithm facilitates its application to multi-degree-of-freedom structures.

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

“…(2021) to optimize the shape of wind‐sensitive structures for aerodynamic mitigation. Based on DRL, Pisarski and Jankowski (2022) presented a semi‐active control method to suppress transient vibrations of structures when subjected to unknown harmonic excitation. Eshkevari et al.…”