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.

翻译：本研究以深度强化学习为起点，提出了一种针对闭环圆柱尾流控制的自学习算法，旨在降低阻力和升力波动，并面临稀疏传感器信息的额外挑战。通过将传感器信号提升为动态特征（DF），即预测未来流动状态的特征，显著提高了深度强化学习（DRL）的性能。由此产生的基于动态特征的DRL（DF-DRL）方法无需动态模型即可自动学习被控对象的反馈控制。结果表明，DF-DRL模型的阻力系数比基于直接传感器反馈的原始模型低25%。更重要的是，仅使用单个表面压力传感器，DF-DRL在Re=100时可将阻力系数降至约8%的先进水平，并显著抑制升力系数波动。因此，DF-DRL允许部署稀疏流动传感而不降低控制性能。该方法在更高雷诺数下控制流动时也表现出良好的鲁棒性：在Re=500和1000时分别将阻力系数降低32.2%和46.55%，表明该方法具有广泛适用性。由于在实际场景中表面压力信息比流速信息更易于测量，本研究为基于壁面压力信号的圆柱主动流动控制实验设计提供了重要参考，这是向实际多输入多输出（MIMO）系统进一步开发智能控制的关键步骤。