Data-Driven Adaptive Control of Transonic Buffet via Localized Morphing Skin

Transonic shock buffet, characterized by large-amplitude self-sustained shock oscillations arising from shock wave/boundary layer interactions, poses significant challenges to aircraft handling quality and structural integrity. Conventional control strategies for buffet suppression typically require prior knowledge of unstable steady-state solutions or time-averaged flow fields and are only applicable to fixed-flow conditions, rendering them inadequate for realistic flight scenarios involving time-varying parameters. This study proposes a data-driven adaptive control framework for transonic buffet suppression utilizing localized morphing skin as the actuation mechanism. The control system employs a Multi-Layer Perceptron neural network that dynamically adjusts the local skin height based on lift coefficient feedback, with the target lift coefficient determined through a moving average method. Numerical simulations on the NACA0012 airfoil demonstrate that the optimal actuator configuration—a skin length of 0.2c with maximum deformation positioned at 0.65c—achieves effective buffet suppression with minimal settling time. Beyond this baseline case, the proposed method exhibits robust performance across different flow conditions. Furthermore, the controller successfully suppresses buffet under time-varying flow conditions, including simultaneous variations in Mach number and angle of attack. These results demonstrate the potential of the proposed framework for practical aerospace applications.