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Abstract Bird flight is often characterized by outstanding aerodynamic efficiency, agility and adaptivity in dynamic conditions. Feathers play an integral role in facilitating these aspects of performance, and the benefits feathers provide largely derive from their intricate and hierarchical structures. Although research has been attempted on developing membrane-type artificial feathers for bio-inspired aircraft and micro air vehicles (MAVs), fabricating anatomically accurate artificial feathers to fully exploit the advantages of feathers has not been achieved. Here, we present our 3D printed artificial feathers consisting of hierarchical vane structures with feature dimensions spanning from 10⁻²to 10²mm, which have remarkable structural, mechanical and aerodynamic resemblance to natural feathers. The multi-step, multi-scale 3D printing process used in this work can provide scalability for the fabrication of artificial feathers tailored to the specific size requirements of aircraft wings. Moreover, we provide the printed feathers with embedded aerodynamic sensing ability through the integration of customized piezoresistive and piezoelectric transducers for strain and vibration measurements, respectively. Hence, the 3D printed feather transducers combine the aerodynamic advantages from the hierarchical feather structure design with additional aerodynamic sensing capabilities, which can be utilized in future biomechanical studies on birds and can contribute to advancements in high-performance adaptive MAVs. 

Abstract Uncrewed aerial vehicles are integral to a smart city framework, but the dynamic environments above and within urban settings are dangerous for autonomous flight. Wind gusts caused by the uneven landscape jeopardize safe and effective aircraft operation. Birds rapidly reject gusts by changing their wing shape, but current gust alleviation methods for aircraft still use discrete control surfaces. Additionally, modern gust alleviation controllers challenge small uncrewed aerial vehicle power constraints by relying on extensive sensing networks and computationally expensive modeling. Here we show end-to-end deep reinforcement learning forgoing state inference to efficiently alleviate gusts on a smart material camber-morphing wing. In a series of wind tunnel gust experiments at the University of Michigan, trained controllers reduced gust impact by 84% from on-board pressure signals. Notably, gust alleviation using signals from only three pressure taps was statistically indistinguishable from using six pressure tap signals. By efficiently rejecting environmental perturbations, reduced-sensor fly-by-feel controllers open the door to small uncrewed aerial vehicle missions in cities. 

Abstract Some bird species fly inverted, or whiffle, to lose altitude. Inverted flight twists the primary flight feathers, creating gaps along the wing’s trailing edge and decreasing lift. It is speculated that feather rotation-inspired gaps could be used as control surfaces on uncrewed aerial vehicles (UAVs). When implemented on one semi-span of a UAV wing, the gaps produce roll due to the asymmetric lift distribution. However, the understanding of the fluid mechanics and actuation requirements of this novel gapped wing were rudimentary. Here, we use a commercial computational fluid dynamics solver to model a gapped wing, compare its analytically estimated work requirements to an aileron, and identify the impacts of key aerodynamic mechanisms. An experimental validation shows that the results agree well with previous findings. We also find that the gaps re-energize the boundary layer over the suction side of the trailing edge, delaying stall of the gapped wing. Further, the gaps produce vortices distributed along the wingspan. This vortex behavior creates a beneficial lift distribution that produces comparable roll and less yaw than the aileron. The gap vortices also inform the change in the control surface’s roll effectiveness across angle of attack. Finally, the flow within a gap recirculates and creates negative pressure coefficients on the majority of the gap face. The result is a suction force on the gap face that increases with angle of attack and requires work to hold the gaps open. Overall, the gapped wing requires higher actuation work than the aileron at low rolling moment coefficients. However, above rolling moment coefficients of 0.0182, the gapped wing requires less work and ultimately produces a higher maximum rolling moment coefficient. Despite the variable control effectiveness, the data suggest that the gapped wing could be a useful roll control surface for energy-constrained UAVs at high lift coefficients. 

Smooth camber morphing aircraft offer increased control authority and improved aerodynamic efficiency. Smart material actuators have become a popular driving force for shape changes, capable of adhering to weight and size constraints and allowing for simplicity in mechanical design. As a step towards creating uncrewed aerial vehicles (UAVs) capable of autonomously responding to flow conditions, this work examines a multifunctional morphing airfoil’s ability to follow commands in various flows. We integrated an airfoil with a morphing trailing edge consisting of an antagonistic pair of macro fiber composites (MFCs), serving as both skin and actuator, and internal piezoelectric flex sensors to form a closed loop composite system. Closed loop feedback control is necessary to accurately follow deflection commands due to the hysteretic behavior of MFCs. Here we used a deep reinforcement learning algorithm, Proximal Policy Optimization, to control the morphing airfoil. Two neural controllers were trained in a simulation developed through time series modeling on long short-term memory recurrent neural networks. The learned controllers were then tested on the composite wing using two state inference methods in still air and in a wind tunnel at various flow speeds. We compared the performance of our neural controllers to one using traditional position-derivative feedback control methods. Our experimental results validate that the autonomous neural controllers were faster and more accurate than traditional methods. This research shows that deep learning methods can overcome common obstacles for achieving sufficient modeling and control when implementing smart composite actuators in an autonomous aerospace environment.