Reinforcement Learning, Adaptive Control, MPC for Vibration Control: A Survey
Abstract
It synthesizes progress across structural, aerospace, mechanical, and civil engineering applications, addressing key challenges e.g., model
uncertainty, nonlinear dynamics, sensing and actuation constraints, and real-world deployment considerations. The review highlights several
developments: actor-critic and off-policy RL methods extending optimal control to data-rich environments; adaptive control techniques ensuring robustness against unmodeled dynamics; MPC facilitating constraint handling and preview capabilities; and modal control retaining
its essential role in managing lightly damped flexible structures. A comparative analysis of algorithms is provided, along with a summary of
experimental validations. The article concludes by identifying open research challenges in safety assurance, sample efficiency, integration
with physical principles, and the establishment of trustworthy benchmarking practices.
Keywords
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DOI: https://doi.org/10.18686/utc.v11i3.274
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