7卷 第1期
National Center for Applied Mathematics in Chongqing, Chongqing Normal University, Chongqing, 401331, China
纸质出版：2026-01-30
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Jitian Xie. (2026). Reinforcement Learning, Adaptive Control, MPC for Vibration Control: A Survey. 科技理论与实践（英文版）, 7(1), 73-76.
Xie Jitian.Reinforcement Learning, Adaptive Control, MPC for Vibration Control: A Survey[J].Theory and Practice of Science and Technology,2026,07(01):73-76.
Jitian Xie. (2026). Reinforcement Learning, Adaptive Control, MPC for Vibration Control: A Survey. 科技理论与实践（英文版）, 7(1), 73-76. DOI： 10.47297/taposatWSP2633-456915.20260701.
Xie Jitian.Reinforcement Learning, Adaptive Control, MPC for Vibration Control: A Survey[J].Theory and Practice of Science and Technology,2026,07(01):73-76. DOI： 10.47297/taposatWSP2633-456915.20260701.
This paper reviews reinforcement learning (RL)
adaptive control
model predictive control (MPC)
and modal control strategies. 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.
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