Adaptive control method for morphing trailing-edge wing based on deep supervision network and reinforcement learning

Abstract

The morphing trailing-edge wing can adaptively change its shape according to the different flight conditions, improving cruising efficiency. The control of the morphing trailing edge is a high-dimensional variable and nonlinear problem, facing the difficulty of the high training cost and the difficulty in real-time continuous control. To overcome these difficulties, a novel adaptive control method based on the couple of Deep Supervision Net (DSN) and Deep Reinforcement Learning (DRL) is proposed to solve a control problem of the morphing trailing-edge wing. DSN uses distributed loads to improve aerodynamic prediction accuracy compared to the traditional surrogate models, while DRL conducts real-time control and improves the performance at non-design points compared to the traditional multi-design points optimization. After control, the lift-to-drag ratio at the start point can be increased by 1.36%, and can be increased by 2.61% at the end point tested on a commercial wide-body aircraft model.