mlsanityData to 7 Oct 2026

Dynamic Feature-based Deep Reinforcement Learning for Flow Control of Circular Cylinder with Sparse Surface Pressure Sensing

arXiv 2307.01995 ↗First posted 5 Jul 2023cs.LG physics.flu-dyncited by 7

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Abstract (arXiv)

This study proposes a self-learning algorithm for closed-loop cylinder wake control targeting lower drag and lower lift fluctuations with the additional challenge of sparse sensor information, taking deep reinforcement learning as the starting point. DRL performance is significantly improved by lifting the sensor signals to dynamic features (DF), which predict future flow states. The resulting dynamic feature-based DRL (DF-DRL) automatically learns a feedback control in the plant without a dynamic model. Results show that the drag coefficient of the DF-DRL model is 25% less than the vanilla model based on direct sensor feedback. More importantly, using only one surface pressure sensor, DF-DRL can reduce the drag coefficient to a state-of-the-art performance of about 8% at Re = 100 and significantly mitigate lift coefficient fluctuations. Hence, DF-DRL allows the deployment of sparse sensing of the flow without degrading the control performance. This method also shows good robustness in controlling flow under higher Reynolds numbers, which reduces the drag coefficient by 32.2% and 46.55% at Re = 500 and 1000, respectively, indicating the broad applicability of the method. Since surface pressure information is more straightforward to measure in realistic scenarios than flow velocity information, this study provides a valuable reference for experimentally designing the active flow control of a circular cylinder based on wall pressure signals, which is an essential step toward further developing intelligent control in realistic multi-input multi-output (MIMO) system.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 22 of its 75 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

22 of 75 references.

References · 22

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10 more
  1. 2018An Introduction to Deep Reinforcement Learningcited by 201
  2. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 139
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  9. 2021A Vision Based Deep Reinforcement Learning Algorithm for UAV Obstacle Avoidancecited by 8
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 2; 2026: 2.

7 papers.

  1. 2024Multi-agent reinforcement learning for the control of three-dimensional Rayleigh-B'enard convectioncited by 8
  2. 2024Navigation in a simplified Urban Flow through Deep Reinforcement Learningcited by 5
  3. 2024Multi-modal Policies with Physics-informed Representations in Complex Fluid Environmentscited by 1
  4. 2025Data-assimilated model-informed reinforcement learningcited by 1
  5. 2025Shocks Under Control: Taming Transonic Compressible Flow over an RAE2822 Airfoil with Deep Reinforcement Learningcited by 1
  6. 2026Hypersonic Flow Control: Generalized Deep Reinforcement Learning for Hypersonic Intake Unstart Control under Uncertaintycited by 0
  7. 2026Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitionscited by 0