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Reinforcement learning‐based control to suppress the transient vibration of semi‐active structures subjected to unknown harmonic excitation
Author(s) -
Pisarski Dominik,
Jankowski Łukasz
Publication year - 2023
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12920
Subject(s) - reinforcement learning , control theory (sociology) , transient (computer programming) , computer science , controller (irrigation) , optimal control , harmonic , iterative learning control , heuristic , vibration control , dissipation , vibration , control (management) , artificial intelligence , mathematics , mathematical optimization , physics , operating system , thermodynamics , quantum mechanics , agronomy , biology
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.