Wind Defiant Morphing Drones

Abstract

Intense winds are a challenge for vertical take‐off and landing drones with wings. In particular, in the hovering regime, wings are sensitive to wind currents that can be detrimental to their operational and energetic performances. Tail‐sitters are particularly prone to those wind currents because their wings are perpendicular to the incoming wind during hovering. This wind generates a large amount of drag and can displace and destabilize the vehicle, possibly leading to catastrophic failures. Herein, our morphing strategy demonstrates in a custom‐built 1.8 kg tail‐sitter with morphing wings that can actively resist winds and leverage them to increase its aerodynamic efficiency. It is shown that adaptive wing morphing during hovering in adverse wind conditions can reduce normalized energy consumption up to 85%, increase attitude and positional stability, and leverage wind energy to increase its yaw angular rate up to 200% while decreasing motor saturation levels.

Employing Wing Morphing to Cooperate Aileron Deflection Improves the Rolling Agility of Drones

“…In the nonlinear Equation ( 8) and ( 9), the terms including S represent the additional forces/moments produced by the CG shift as the wing sweeping, and the terms including first and second derivatives of static moment are the additional forces and moments produced by the velocity and acceleration of aircraft's CG shift in wing asymmetric sweeping progress. The aerodynamic model is introduced, and the forces and moments exert on the drone are as follows: (10) where g refers to the gravitational acceleration, P indicates the pull of propeller, L, D, and Y correspond to the lift, drag, and lateral force of the UAV, respectively. And l A , m A , and n A , respectively, stand for the roll, pitch, and yaw aerodynamic moment.…”

“…[ 9 ] During crosswind or gusty conditions, a larger control margin can enhance the drone's ability to maintain attitude. [ 10 ] To make the winged UAV more flexible, researchers have explored the application of asymmetric wing morphing for roll control in drones, and some have even conducted flight tests on prototypes. [ 11 ] There are two types of roll control methods for existing morphing wing drones: 1) methods based on wing deformation alone, and 2) methods based on the cooperation of aerodynamic control surfaces and wing morphing.…”

“…[ 21 ] All drones that have undergone flight testing only use simple overlays of wing deformation and aerodynamic control surfaces, and coordinating them for better control performance has not yet been explored. [ 10–12,16–18,20 ]…”

“…[21] All drones that have undergone flight testing only use simple overlays of wing deformation and aerodynamic control surfaces, and coordinating them for better control performance has not yet been explored. [10][11][12][16][17][18]20] The main innovation of this article is to establish a cooperative strategy of wing deformation and aileron to maximize the roll rate of drones, and it is proven in flight experiments that this combination control can produce more than 2 times the roll rate of aileron control when cruising at an AOA of 4°. The roll rate is primarily influenced by two factors: the roll moment and the drone's inertia.…”

Trajectory generation and tracking control for aggressive tail-sitter flights

“…(2) Existing controllers either ignore wind effect in the environment (e.g., Ritz and D’Andrea (2017); Lustosa (2017)), or compensate the disturbance through incremental control updates from increased control error (e.g., Smeur et al (2020); Tal and Karaman (2022)), while our proposed approach incorporates wind effect by adjusting the reference trajectory (e.g., attitude) to maintain coordinated flight based on the differential flatness and then tracks the adjusted trajectory in real time. Given the considerable aerodynamic efficiency loss of tail-sitter in windy conditions (Vourtsis et al, 2023), our proposed feedforward strategy compensates the wind effect in a pre-emptive way before the control error actually accumulates. (3) Existing controllers (Ritz and D’Andrea, 2017; Tal and Karaman, 2022) simultaneously track trajectories and process singularities, while our work decouples singularities from the tracking controller, by the two-stage architecture.…”