Reinforcement learning for gust load control of an elastic wing via camber morphing at arbitrary sinusoidal gusts

• A new approach for gust load alleviation based on wing morphing. • A highly efficient reinforcement learning training process for gust load alleviation. • A CFD-based fluid-structure-control simulation for RL-based GLA control validation. • The flow mechanism and generalizability investigated in the high-fidelity environment. This paper presents the process of training and validation of deep reinforcement learning (RL) for gust load alleviation (GLA) based on camber morphing. First, a simplified aeroelastic model of the morphing wing considering the gust input is established based on the doublet-lattice method. The simplified model is adopted for GLA control training, and the proximal policy optimization algorithm is employed. When training is completed, the RL-based GLA controller is evaluated in the high-fidelity fluid-structure interaction environment. Results show that the controller effectively suppresses both the structural load and the aerodynamic force. It alleviates wingtip acceleration by 77.4 % at A g =1m/s, f g =2 Hz. The flow field suggests that the wing morphing counteracts the pressure distribution change induced by gusts and suppresses the lift fluctuations, thus alleviating the wingtip acceleration. Then, the RL-based GLA controller is tested at sinusoidal gust frequencies 1.5–3 Hz and amplitudes 1–4m/s, demonstrating a respectable alleviation effect within the maximum morphing angle. Finally, the comparison with the traditional PI controller further proves its superior gust alleviation performance.