Asymmetric Airfoil Morphing via Deep Reinforcement Learning

Morphing aircraft are capable of modifying their geometry configurations according to different flight conditions to improve their performance, such as by increasing the lift-to-drag ratio or reducing their fuel consumption. In this article, we focus on the airfoil morphing of wings and propose a novel morphing control method for an asymmetric deformable airfoil based on deep reinforcement learning approaches. Firstly, we develop an asymmetric airfoil shaped using piece-wise Bézier curves and modeled by shape memory alloys. Resistive heating is adopted to actuate the shape memory alloys and realize the airfoil morphing. With regard to the hysteresis characteristics exhibited in the phase transformation of shape memory alloys, we construct a second-order Markov decision process for the morphing procedure to formulate a reinforcement learning environment with hysteresis properties explicitly considered. Subsequently, we learn the morphing policy based on deep reinforcement learning techniques where the accurate information of the system model is unavailable. Lastly, we conduct simulations to demonstrate the benefits brought by our learning implementations and validate the morphing performance of the proposed method. The simulation results show that the proposed method provides an average 29.8% performance improvement over traditional methods.