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Focusing on the typical problem of flow around a circular cylinder, we propose an active flow control method of reducing drag of a circular cylinder, in which a deep reinforcement learning (DRL) method is used to establish the closed-loop control strategy with pressure sensors providing feedback signals. The detailed comparisons of the lift, drag, and flow fields with and without control are conducted. In the control system, pressure sensors evenly distributed on the cylinder surface are used to provide feedback signals for the controller. The multilayer perceptron is adopted to establish the mapping relationship between the sensors and the blowing/suction jets, i.e. the control strategy. A pair of continuously adjustable synthetic jets that exert  transverse force mainly  on the top and bottom edge of the cylinder is implemented. Based on the state-of-the-art proximal policy optimization algorithm, the control strategy is explored and optimized during a large number of learning episodes, thus achieving an effective, efficient, and robust drag reduction strategy. To build up the high-fidelity numerical environment, we adopt the lattice Boltzmann method as a core solver, which, together with the DRL agent, establishes an interactive framework. Furthermore, the surface pressure signals are extracted during the unsteady simulation to adjust the real-time blowing/suction jets intensity. The lift information and the drag information are recorded to evaluate the performance of the current control strategy. Results show that the active control strategy learnt by the DRL agent can reduce the drag by about 4.2% and the lift amplitude by about 49% at Reynolds number 100. A strong correlation between the drag reduction effect of the cylinder and the elongated recirculation bubble is noted. In addition, the drag reduction rate varies over a range of Reynolds numbers. The active control strategy is able to reduce the drag by 17.3% and 31.6% at Reynolds number 200 and 400, respectively. Owing to the fact that wall pressure signals are easy to measure in realistic scenarios, this study provides valuable reference for experimentally designing  the active flow control of a circular cylinder based on wall pressure signals and intelligent control in more complicated flow environments.

Artificially intelligent control of drag reduction around a circular cylinder based on wall pressure feedback