Active flutter suppression for a flexible wing model with trailing-edge circulation control via reinforcement learning

Previous attempts at active flutter suppression have been based on driving the deflection of multiple pairs of discontinuous mechanical control surfaces. Here, we explore the effects of trailing-edge Circulation Control (CC) for flutter control on flexible wings. To avoid the problem that the nonlinear aeroelastic model is difficult to establish accurately, we trained a closed-loop control strategy based on the model-free deep reinforcement learning algorithm through aeroelastic wind tunnel testing. The results show that the strategy can intelligently select the appropriate jet intensity according to the real-time state of the flexible wing. The oscillation amplitude of flutter can be reduced by 92%. The air consumption required for unsteady CC to suppress flutter is reduced by 37% compared to steady CC. This study aims to provide an innovative control method and strategy for active flutter suppression of large aspect ratio flexible wings.