A Distributed Nonlinear Model Predictive Control (DNMPC) approach is proposed to control the simplified decoupled dynamics of a quadrotor UAV. The performance of DNMPC is compared, in terms of tracking and execution time, to that of standard control configurations based on centralized MPC and PID control. The aim is to show the suitability of each configuration in terms of performance and practicality in real-time applications. The results show the advantage of using DNMPC in terms of ease of tuning and computational cost over more centralized feedback control approaches. For extra realism, wind disturbances and sensor noise are incorporated into the simulations.

This research investigates the fluid-structure interaction phenomenon of gravity-driven falling rigid plates through a combination of experimental and theoretical approaches. Plates of varying dimensions and densities are systematically examined to explore the influence of non-dimensional parameters, including the Reynolds number ( R e ) and dimensionless moment of inertia ( I * ), on the falling patterns. High-speed photography is employed to extract plate trajectories and posterior kinematics calculations. In the range of relatively high Reynolds numbers ( R e > 800 ), our study identifies three distinct falling modes: periodic fluttering, periodic tumbling, and marginal chaotic motion. The falling trajectories of the plates are analyzed and compared with their corresponding kinematic behaviors. By integrating theoretical analyses with experimental findings, we develop a semi-analytical model capable of calculating the real-time hydrodynamic forces and moments acting on falling plates. This model facilitates the prediction of falling trajectories for quasi-2-dimensional plates with arbitrary material and dimension combinations. Comparisons between model predictions and experimental results demonstrate good agreement in the fluttering and tumbling modes.

Hummingbirds and many flying biological organisms use a method known as wing kinematics modulation (WKM) for flight control and stability. This technique involves actively varying the wing flapping kinematics during flight to generate control forces and moments in response to desired trajectories, external perturbations, and natural instabilities. Recently, we designed, developed, and free-flight tested a biomimetic robotic hummingbird which uses these methods for flight control. For longitudinal control, two methods were implemented: (1) flap plane tilting which generates a coupled pitching moment and horizontal force, and (2) wing stroke mean shifting, which moves the longitudinal position of the aerodynamic center relative to the center of gravity, generating a pure pitching moment. The robot was flight tested in hover using each of these control methods. The first method resulted in higher translational velocities, larger attitude angles, and higher pitch rates, as well as off-axis roll and yaw rates. The second method resulted in significantly less movement. These results suggest that the plane tilting method is best for introducing larger changes in states, while the mean shifting method is best for more precise hovering. This is the first experimental study to quantify the effects of biological flight control strategies on the hovering flight of a two-winged, free-flying robotic hummingbird. These results could be used to inform roboticist on the best methods to use for controlling the longitudinal dynamics of flapping wing robots, as well as derive control schemes that leverage the two methods for quick and efficient execution of flight maneuvers.

The paper discusses flight-test-based system identification of a compact, re-configurable, rotary-wing micro air vehicle capable of sustained hover and could potentially be launched from a 40-mm grenade launcher. The objective was to extract a linear time-invariant model of the system to gain an understanding of a novel coaxial helicopter. The vehicle design features a cylindrical fuselage, coaxial rotors with foldable/deployable blades, thrust-vectoring mechanism for pitch/roll control, and differential rotational speed for yaw control. Flight experiments were conducted to excite the vehicle’s longitudinal, lateral, directional, and heave modes from a hovering state. A linearized state-space model was extracted from the flight test data. The model showed that the lateral and longitudinal dynamic modes were decoupled from each other and from the other modes. Due to the axisymmetric vehicle design, the longitudinal and lateral stability and control coefficients and their eigenvalues were nearly identical. All the aerodynamic damping terms were negative and stabilizing except for the pitch and roll acceleration modes, which necessitated the need for pitch and roll feedback control.

This paper optimizes the aerodynamic performance of quadcopter UAVs by varying the cross-section shape of the fuselage arm. In detail, a testing stand is set up to measure simultaneously the lift force as well as the input electric power. The tested quadcopter UAV has replaceable fuselage arms, allowing convenient testing for different arm cross-sections fabricated by 3D printing. The optimization process starts from a comparison between two basic cross-section shapes: circular arm and square arm. Results show that the square arm has an overall lift-to-power ratio 2.41% higher than the circular arm with the same weight. Then, by inclining and setting up curves on the side surfaces of a square arm, the optimized rhombus + 15°+arc2 arm is proposed to have an overall 3.68% increment in lift-to-power ratio and 5.77% increment in Figure of Merit (FM) as compared to the circular arm.

This study aims at the flexible landing control problem of vertical take-off and landing UAV. A control method based on Model Reference Adaptive Control (MRAC) is designed and verified to ensure the stability of the UAV when landing on the moving platform and reduce control errors and dynamic instability caused by bouncing. By establishing a mathematical model including landing gear stiffness and damping parameters, and combining it with MRAC for adaptive parameter adjustment, the system can adapt to different landing conditions in real time, ensuring the stability and feasibility of flexible landing. This study conducted experimental tests on multiple sets of stiffness and damping parameters and analyzed the effectiveness of the MRAC control strategy under different configurations through numerical simulation. Experimental results show that when the stiffness and damping configuration are appropriate, MRAC can quickly adjust the control parameters, so that the time domain response characteristics of the UAV tend to be stable when landing, and the loss function shows a decreasing trend, proving that the control method has good convergence characteristics and adaptability. This study analyzes the MRAC parameter adjustment process through game theory and proves that the system can achieve Nash equilibrium under certain conditions, making each landing gear control strategy optimal and further improving landing stability. In order to verify the feasibility of MRAC control in practical applications, this study also considered the effects of sensing errors and random noise. The results show that the method can still successfully converge, demonstrating its robustness under different environmental conditions. This study confirms the applicability of MRAC in the flexible landing control of UAV and provides a theoretical basis for the future development of dynamic stiffness and damping adaptation mechanisms.

This paper investigates the impact of structural flexibility on the thermal soaring performance of a mini-UAV wing. Three optimization problems are formulated to identify the optimal set of structural parameters that maximize energy extraction in both steady and unsteady soaring scenarios. The ASWING aeroelastic simulation tool is first validated experimentally and then employed to solve these optimization problems. Results suggest that increased structural flexibility yields significant benefits only in unsteady soaring (dolphin-kick strategy), but not in steady thermal soaring. For the latter, the apparent performance gains from structural optimization stem more from an effective change in the rigid geometry than from actual aeroelastic advantages. Furthermore, it is shown that active pitch control can yield equal or superior benefits compared to passive structural adaptations. These findings highlight the limitations of flexible wing designs for general-purpose soaring UAVs and suggest that robust aerodynamic geometry coupled with smart control may be a more practical design direction.

Flexible wings, serving as the key components of tailless flapping wing micro air vehicles (FWMAVs), simultaneously generate lift, thrust, and control torques. Due to the complex unsteady fluid-structure interactions involved in their flapping, accurately predicting their aerodynamic performance, such as mean lift and lift-to-power efficiency, becomes challenging. There is also a lack of widely accepted and rational design methods for flexible wings. To address these, we propose an experimental optimization design method based on response surfaces methodology and investigate the impact of four design parameters—aspect ratio ( A ), slack angle ( θ ), taper ratio ( λ ), and flapping frequency ( f )—on the aerodynamic performance of flexible wings. The results show that the models accurately predict the aerodynamic performance of flexible wings, with an error margin of less than 10% compared to experimental measurements. Utilizing these models, an optimal flexible wing for a tailless FWMAV with a mass of 15 g was designed and manufactured, which can generate 15.24 gf of lift while maintaining a lift-to-power efficiency of 6.07 gf/W. Additionally, the models indicate that the four parameters are nearly equally important for the aerodynamic performance of flexible wings, and the coupling between these parameters also significantly affects the aerodynamic performance. Specifically, A & λ , A & f , θ & f , and λ & f affect mean lift, while A & λ , θ & λ , and θ & f affect lift-to-power efficiency. These coupling effects help explain the contradictions found in previous studies regarding the influence of different parameters. Our research provides clear guidance and practical methods for designing flexible wings in tailless FWMAVs.

Ground effect (GE) behavior occurs when a hover-capable multirotor aerial vehicle, such as a quadcopter, flies within close proximity to the ground and the vehicle experiences an increase in thrust despite constant power being applied to the propellers. Current GE models assume that the ground plane is flat and smooth. This paper investigates the influence of aerodynamically-rough surfaces on GE behavior for standard two-blade propellers under quasi-steady hover conditions. First, a nondimensional model is proposed that incorporates the aerodynamic roughness and zero-plane displacement height of a rough surface with GE parameters previously found in the literature. Second, a GE model that accounts for surface roughness is described. Third, physical experiments are conducted to quantify the aerodynamic properties of controlled rough surfaces and the GE strength through observations of in-ground effect (IGE) and out-of-ground effect (OGE) thrusts produced by commercially available propellers. The results show that aerodynamically rougher surfaces corresponded to higher IGE thrust. Fourth, statistical analysis of the results supported the accuracy of the proposed model, where the average root-mean-squared error is 0.90% with an average maximum error of 2.39% over all test scenarios. Finally, nondimensional analysis confirmed that when similarity conditions are met, the proposed model follows theoretical projections. These findings can be exploited for vehicle motion control, navigation, and design.

Moving Horizon Estimation (MHE) offers multiple advantages over Kalman Filters when it comes to the localization of drones. However, due to the high computational cost, they can not be used on Micro Air Vehicles (MAVs) with limited computational power. We have previously shown, that with a few assumptions and simplifications, MHE can be made more efficient while retaining good localization performance. In this paper, we present two additional improvements: the introduction of dynamic step sizes to the gradient descent algorithm, which leads to a significant increase in robustness, and the use of switching variables for outlier rejection, which further reduces the computational load. Both improvements are implemented and assessed in simulation and experiments. Using dynamic step sizes makes it possible to reliably use the estimator on board a real drone, and the use of Newton’s method specifically opens the option to add different types of measurements. The new outlier rejection method on the other hand is shown to reduce the computational load significantly without impacting the accuracy.