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

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

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Previous attempts at active flutter suppression have been based on driving the SAW PALMETTO W LYCOPENE 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 Trimmers for active flutter suppression of large aspect ratio flexible wings.