Deep Reinforcement Learning Achieves Multifunctional Morphing Airfoil Control

Uncrewed aerial vehicles (UAVs) are growing in popularity for both civilian and military applications, which makes improving their efficiency and adaptability for various aerial environments an attractive objective. Many studies pursue this goal using morphing techniques that incorporate shape changes not typically seen in traditional aircraft. Due to weight and volume constraints consistent with smaller flight vehicles, smart materials, such as macro fiber composites (MFCs), have been used to achieve the desired shape changes. Macro fiber composites are low-profile piezoelectric actuators which have gained substantial attention within the morphing aircraft community. Piezoelectric actuators operate by generating strain when voltage, and hence an electric field, is applied to the electrodes. Piezoelectric actuators are also well known for their capabilities to produce high force-output and a highspeed actuation response. Unlike traditional piezoelectric actuators, which are composed of solid piezoelectric material, MFCs are manufactured using a series of thin piezoceramic rods in a composite laminate layup allowing them to exhibit excellent flexibility while still maintaining the performance benefits attributed to traditional piezoelectric actuators. Furthermore, MFCs exhibit large out-of-plane curvatures when bonded to a thin inextensible substrate, like steel shim, which shifts the structure’s neutral axis. This behavior is attractive for camber morphing airfoil applications and has spurred a large subset of research in the field of morphing aircraft.