Small Fixed-wing Unmanned Aerial Vehicles Research Articles

Experimental Aerodynamics of a Small Fixed-Wing Unmanned Aerial Vehicle Coated with Bio-Inspired Microfibers Under Static and Dynamic Stall

A passive flow control technique in the form of microfiber coatings with a diverging pillar cross-section area was applied to the wing suction surface of a small tailless unmanned aerial vehicle (UAV). The coatings are inspired from ‘gecko feet’ surfaces, and their impact on steady and unsteady aerodynamics is assessed through wind tunnel testing. Angles of attack from −2° to 17° were used for static experiments, and for some cases, the elevon control surface was deflected to study its effectiveness. In forced oscillation, various combinations of mean angle of attack, frequency and amplitude were explored. The aerodynamic coefficients were calculated from load cell measurements for experimental variables such as microfiber size, the region of the wing coated with microfibers, Reynolds number and angle of attack. Microfibers with a 140 µm pillar height reduce drag by a maximum of 24.7% in a high-lift condition and cruise regime, while 70 µm microfibers work best in the stall flow regime, reducing the drag by 24.2% for the same high-lift condition. Elevon deflection experiments showed that pitch moment authority is significantly improved near stall when microfibers cover the control surface and upstream, with an increase in CM magnitude of up to 22.4%. Dynamic experiments showed that microfibers marginally increase dynamic damping in pitch, improving load factor production in response to control surface actuation at low angles of attack, but reducing it at higher angles. In general, the microfiber pillars are within the laminar boundary layer, and they create a periodic slip condition on the top surface of the pillars, which increases the near-wall momentum over the wing surface. This mechanism is particularly effective in mitigating flow separation at high angles of attack, reducing pressure drag and restoring pitching moment authority provided by control surfaces.

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance

Small Fixed-Wing Unmanned Aerial Vehicle Path Following Under Low Altitude Wind Shear Disturbance

Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.

Adaptive PID Control for Autopilot Design of Small Fixed Wing UAVs

The development of a flight control system for the class of small fixed wing Unmanned Aerial Vehicle (UAV) imposes a challenge to the designers to achieve acceptable and stable performance characteristics across the defined flight envelope. This paper focuses on developing a dynamic model of the Aerosonde UAV, incorporating it with an adaptive Proportional, Integral and Derivative (PID) control system for designing both lateral and longitudinal autopilots. The PID gains for each control loop are self-tuned based on an analytical approach, making sure that the desired command value is achieved at each time interval as closely as possible. To check the robust nature of the designed control system in the presence of external wind disturbance, a Dryden wind gust model was developed, and the entire simulation results were carried out in MATLAB®/Simulink environment. The proposed adaptive PID controller proves the effectiveness and robustness of the autopilot control system.

Distributed swarm system with hybrid-flocking control for small fixed-wing UAVs: Algorithms and flight experiments

This paper presents a distributed swarm system for small fixed-wing unmanned aerial vehicles (UAVs). In particular, to perform various missions with multiple UAVs that are densely gathered and collision free, a hybrid-flocking control algorithm is synthesized by using three types of control protocols: vector field guidance (for path following/loitering), augmented Cucker-Smale (ACS) model (for collective flocking behavior), and potential field (for collision avoidance). In particular, to address the issue of conflicts between different control protocols, the adaptive ACS model is proposed and the optimization problem is formulated to determine the suitable mixing weights of control protocols. We also design the transition of multiple operation modes and communication architecture for the swarm system. The system is evaluated using the proposed hybrid-flocking control algorithm by proof-of-concept real flight experiments using 18 small fixed-wing UAVs as well as extensive numerical simulations. Flight experiments are successfully performed for multiple consecutive tasks including the individual task, circular path loitering and elliptical path loitering while avoiding collisions among UAVs.

A Benchmarking of Commercial Small Fixed-Wing Electric UAVs and RGB Cameras for Photogrammetry Monitoring in Intertidal Multi-Regions

Small fixed-wing electric Unmanned Aerial Vehicles (UAVs) are perfect candidates to perform tasks in wide areas, such as photogrammetry, surveillance, monitoring, or search and rescue, among others. They are easy to transport and assemble, have much greater range and autonomy, and reach higher speeds than rotatory-wing UAVs. Aiming to contribute towards their future implementation, the objective of this article is to benchmark commercial, small, fixed-wing, electric UAVs and compatible RGB cameras to find the best combination for photogrammetry and data acquisition of mussel seeds and goose barnacles in a multi-region intertidal zone of the south coast of Galicia (NW of Spain). To compare all the options, a Coverage Path Planning (CPP) algorithm enhanced for fixed-wing UAVs to cover long areas with sharp corners was posed, followed by a Traveling Salesman Problem (TSP) to find the best route between regions. Results show that two options stand out from the rest: the Delair DT26 Open Payload with a PhaseOne iXM-100 camera (shortest path, minimum number of pictures and turns) and the Heliplane LRS 340 PRO with the Sony Alpha 7R IV sensor, finishing the task in the minimum time.

Robust adaptive three-dimensional trajectory tracking control scheme design for small fixed-wing UAVs

This paper aims to tackle the three-dimensional trajectory tracking problems of small fixed-wing unmanned aerial vehicles subject to nonlinearities, uncertainties and wind disturbances via adaptive techniques. The control objective is to efficiently control the thrust and the deflections of control surfaces to ensure the unmanned aerial vehicle arrives at a specified location within a given time frame. However, achieving this goal for small fixed-wing unmanned aerial vehicles can be challenging because the precise dynamic model and several parameters are not accessible, making most existing control strategies unworkable. Motivated by these facts, based on feedback linearization techniques, we derive linear models with equivalent disturbances to describe the translational dynamics without requiring precise aerodynamic force model information. To deal with the dilemmas where the norm bounds of equivalent disturbances depend on control inputs, system states, and unknown disturbances, a novel robust adaptive control strategy is designed for position control. Based on the assumption of two-time separation, the control scheme incorporates two parts, namely, a position controller containing the horizontal-plane and altitude parts and a robust filter-based attitude regulator. Also, to prevent chattering issues, we design a practical and robust adaptive position controller under which the tracking error is ultimately bounded The overall closed-loop stability is theoretically investigated based on the Lyapunov arguments. Hardware-in-loop simulation experiments are performed to testify our developed control scheme.

Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a Random Forest Algorithm

In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.

Path-Following Control of Small Fixed-Wing UAVs under Wind Disturbance

Aiming at the problems of low following accuracy and weak anti-disturbance ability in the three-dimensional path-following control of small fixed-wing Unmanned Aerial Vehicles (UAV), a Globally Stable Integral Sliding Mode Radial Basis Function S-Plane (GSISM+RBF S-Plane) controller is designed. The controller adopts the inner and outer loop mode, the outer loop adopts the Globally Stable Integral Sliding Mode (GSISM) control, and the inner loop adopts the S-Plane control. At the same time, the unknown disturbance in the model is estimated via an RBF neural network. Firstly, the outer loop controller is designed based on the GSISM, and its stability is proved using the Lyapunov theory. Then, the S-Plane controller is designed for the instruction signal of the inner loop. Considering the complexity of the derivation in the S-Plane controller, a second-order differentiator is introduced. Finally, considering the problem of external wind disturbance, the controller is modeled, studied, and processed in order to better reflect the impact of real external wind on UAV path following. Finally, the Globally Stable Sliding Mode (GSSM) control and Globally Stable Integral Sliding Mode S-Plane (GSISM S-Plane) control are used for a comparative experiment. The simulation results show that the designed GSISM+RBF S-Plane controller can accurately track the ideal path compared with the GSSM and GSISM S-Plane controller, and it has good control performance and anti-disturbance performance.

Statistical analysis of the RCS of a small fixed-wing UAV during a typical flight

The presence and importance of of small UAVs (Unmanned Aerial Vehicles) are increasing. With traditional radar detection the target RCS (Radar Cross Section) and its statistical properties are needed in order to design the detector and to assess detection performance. In this paper we make a statistical analysis of the RCS of a certain small fixed-wing UAV using measured data from a typical flight. Using data from a typical flight instead of measurements or electro-magnetic calculations as a function of aspect angle is a convenient method. Our analysis results in mean values, medians and standard deviations, in suitable Weibull probability distributions and in decorrelation times for the RCS, which are necessary for design and assessment of the detection performance.

Development of a YOLO-KCF Coupling Algorithm for Miniature Fixed-Wing UAVs in Target Detection and Tracking

Target detection and tracking represent key challenges facing miniature fixed-wing unmanned aerial vehicles (UAVs), particularly at high cruising speeds. Therefore, this paper proposes a vision-based target detection and tracking algorithm that systematically couples two mainstream methods, namely, you only look once (YOLO) and kernel correlation filter (KCF) algorithms. This combination enables small fixed-wing UAVs to achieve reliable target detection and rapid target tracking. A customized vision-guidance module is constructed to implement this algorithm, and a dual-thread execution mechanism is developed to ensure that the computational resources are used effectively. A miniature fixed-wing UAV experimental platform is also constructed and evaluated. Flight experiments are performed, and the results demonstrate that the developed algorithm can achieve satisfactory detection and tracking accuracy for stationary and moving ground targets in complex environments.

Small Fixed-Wing UAV Radar Cross-Section Signature Investigation and Detection and Classification of Distance Estimation Using Realistic Parameters of a Commercial Anti-Drone System

Various types of small drones constitute a modern threat for infrastructure and hardware, as well as for humans; thus, special-purpose radar has been developed in the last years in order to identify such drones. When studying the radar signatures, we observed that the majority of the scientific studies refer to multirotor aerial vehicles; there is a significant gap regarding small, fixed-wing Unmanned Aerial Vehicles (UAVs). Driven by the security principle, we conducted a series of Radar Cross Section (RCS) simulations on the Euclid fixed-wing UAV, which has a wingspan of 2 m and is being developed by our University. The purpose of this study is to partially fill the gap that exists regarding the RCS signatures and identification distances of fixed-wing UAVs of the same wingspan as the Euclid. The software used for the simulations was POFACETS (v.4.1). Two different scenarios were carried out. In scenario A, the RCS of the Euclid fixed-wing UAV, with a 2 m wingspan, was analytically studied. Robin radar systems’ Elvira Anti Drone System is the simulated radar, operating at 8.7 to 9.65 GHz; θ angle is set at 85° for this scenario. Scenario B studies the Euclid RCS within the broader 3 to 16 Ghz spectrum at the same θ = 85° angle. The results indicated that the Euclid UAV presents a mean RCS value (σ ¯) of −17.62 dBsm for scenario A, and a mean RCS value (σ ¯) of −22.77 dBsm for scenario B. These values are much smaller than the values of a typical commercial quadcopter, such as DJI Inspire 1, which presents −9.75 dBsm and −13.92 dBsm for the same exact scenarios, respectively. As calculated in the study, the Euclid UAV can penetrate up to a distance of 1784 m close to the Elvira Anti Drone System, while the DJI Inspire 1 will be detected at 2768 m. This finding is of great importance, as the obviously larger fixed-wing Euclid UAV will be detected about one kilometer closer to the anti-drone system.

Physical Modeling, Simulation and Validation of Small Fixed-Wing UAV

The flight dynamics of small Unmanned Aerial Vehicles (UAVs) exhibits substantial nonlinear features which should not be ignored in simulation or analysis. In this work, a complete six degrees of freedom (6DOF) and high-fidelity simulation model of a case study of a small fixed-wing UAV is developed. To accurately estimate the nonlinear UAV’s mathematical model, the mass-inertia properties are investigated through experiments. Moreover, the aerodynamic model characteristics are estimated using a semi-empirical technique to estimate the aerodynamic coefficients and derivatives. The propulsion system’s physical characteristics are estimated and analyzed through experimental measurements. The International Standard Atmosphere (ISA) tables are used to develop the atmospheric model. Furthermore, the actuation model is estimated and validated experimentally through system identification techniques. These estimated sub-model data are integrated to complete the nonlinear flight dynamics model in MATLAB® (Simulink). The UAV trim calculations are graphically derived and then the developed model is tested and verified. The open loop simulation results have proved the superiority of the utilized model techniques to describe the UAV motion. Moreover, it gives very optimistic results to test the flight dynamics of the UAV and design a consistent flight control and guidance system.

Robust horizontal-plane formation control for small fixed-wing UAVs

The formation control problems of small fixed-wing(FW) unmanned aerial vehicles (UAVs) are investigated in this paper. By adopting a simplified aerodynamic force model and a cascade control structure, the horizontal motion of a FW UAV can be described as a linear system with uncertainties based on the feedback linearization technique. Thereafter, a novel robust formation protocol is proposed, which incorporates two components: the nominal controller and the robust filter-based compensator to restrain the influences of equivalent uncertainties. It turns out the formation error can converge to a sufficiently small neighborhood of the origin. The stability of the closed-loop system is proved in combination with classic Lyapunov-based arguments. Finally, field formation flight experiments on four small FW UAVs are carried out to demonstrate the effectiveness of the theoretical results.

Automatic Tuning and Turbulence Mitigation for Fixed-Wing UAV with Segmented Control Surfaces

Unlike bigger aircraft, the small fixed-wing unmanned aerial vehicles face significant stability challenges in a turbulent environment. To improve the flight performance, a fixed-wing UAV with segmented aileron control surfaces has been designed and deployed. A total of four ailerons are attached to the main wing and grouped into inner and outer aileron pairs. The controllers are automatically tuned by utilizing the frequency response data obtained via the frequency sampling filter and the relay with embedded integrator experiments. The hardware validation experiments are performed in the normal and turbulent flight environments under three configurations: inner aileron pair only, outer aileron pair only and collective actuation of all the aileron pairs. The error-threshold-based control is introduced to handle collective actuation of aileron pairs. The experiments have manifested that the collective usage of all aileron segments improves the roll attitude stability by a margin of 38.69% to 43.51% when compared to the independent actuation of aileron pairs in a turbulent atmosphere.

Automatic terminal guidance for small fixed‐wing unmanned aerial vehicles

AbstractThe research presented in this article focuses on expanding and deepening the prior research of a low‐cost terminal guidance system in a previous paper entitled “Design, implementation and verification of a low‐cost terminal guidance system for small fixed‐wing UAVs.” An automatic terminal guidance workflow is specially designed for an individual in a small fixed‐wing unmanned aerial vehicle (SUAV) swarm. The extended work around the proposed workflow primarily involves upgrading onboard hardware modules to improve sensor accuracy and environmental adaptability, the imaging performance of the seeker, as well as the computational capability of the image processor, applying object detection to replace the human‐in‐the‐loop function and adopting the integral proportional guidance law in the vertical direction to reduce the required overload and obtain a larger impact angle. Furthermore, we conducted several field tests on two types of SUAV against a stationary target on the ground in a field scenario. The experiments have generated valuable onboard image data and SUAV status information, all of which are aligned in the time domain. The only remaining data sets that support the findings of this study are available from the corresponding author. Our study into automatic terminal guidance has yielded a solution of the automatic strap‐down monocular terminal guidance problem of individual SUAVs. The field trials of a single SUAV demonstrate the robustness and efficiency of the proposed automatic terminal guidance methodology and lays a foundation for the future SUAVs' cooperative attack test.

$\mathcal {L}{}_{1}$ Adaptive Path-Following of Small Fixed-Wing Unmanned Aerial Vehicles in Wind

This article proposes an adaptive path-following controller of small fixed-wing unmanned aerial vehicles (UAVs) in the presence of wind disturbances, which explicitly considers that wind speed is time-varying. The main idea was to formulate UAVs path-following as control design for systems with parametric uncertainties and external disturbances. Assuming that there is no prior information on wind, the proposed solution is based on the $\mathcal {L}{}_{1}$ adaptive control, using linearized model dynamics. This approach makes clear statements for performance specifications of the controller and relaxes the common constant wind velocity assumption. This makes the design more realistic and the analysis more rigorous, because in practice wind is usually time varying (windshear, turbulence, and gusting). The path-following controller was demonstrated in flight under wind speed up to $10 \text{ m/s}$, representing 50% of the nominal UAV airspeed.

Globally Stable Attitude Control and Quasi-Static Disturbance Estimation in the Presence of Aerodynamic Dissipation

In this letter, we design and verify a novel nonlinear attitude controller for small fixed-wing unmanned aerial vehicles (UAVs) in the presence of dissipation. The attitude controller is designed using quaternions, which provide the physical insight for the control design by relating it to the geometry of rotations. We also show how the controller can be suitably modified to adaptively estimate quasi-static disturbances and compensate for aerodynamic damping by incorporating suitable refinements to the controller. The performance of the controller is then verified using simulations that demonstrate global stability against arbitrarily large initial attitude errors.

Model-Free Integrated Navigation of Small Fixed-Wing UAVs Full State Estimation in Wind Disturbance

This paper presents a model-free distributed multi-sensor extended Kalman filter (DMSEKF) full state estimation algorithm to provide long-term convergent flight parameters for small fixed-wing unmanned aerial vehicles (UAVs). The full state has the attitude, velocity, position, airspeed, and 2D horizontal wind speed. The airspeed and wind speed are estimated in wind disturbance to provide more robust perception information. The model-free estimator has a low-cost standard sensor suite, including an IMU, a magnetometer, a barometer, a GPS module, and an airspeed tube, rather than the aerodynamic model of the UAVs to increase the multi-sensor fusion algorithm versatility in various UAVs. Then, the full state integrated navigation model is established based on the onboard sensor suite fused by the distributed tightly-coupled EKF. In addition, a consistent multiple sensors data processing method is designed to synchronize the time node of all onboard sensors. Finally, the proposed algorithm is verified through the experimental flight sensor data. The results demonstrate that the proposed algorithm can provide a reliable full state vector and achieve an effective solution performance during the UAVs flight application.

Modeling, System Measurements and Controller Investigation of a Small Battery-Powered Fixed-Wing UAV

In this paper, a complete set of nonlinear modeling and controller design process for a small electric fixed-wing unmanned aerial vehicle (UAV) is presented. The nonlinear mathematical model and aerodynamic model of the small fixed-wing UAV are derived. The computational fluid dynamics (CFD) method was used to obtain the aerodynamic coefficients of the UAV, and the models of propulsion system components were established through experiments. Since the linearized and decoupled model of the fixed-wing UAV has a large error, a nonlinear model is established based on Simulink, which is utilized to design and verify the control algorithms. Based on the established nonlinear model, a stability controller, path following controller and path management controller of the aircraft are set up. The results indicate that system parameters of the aircraft can be quickly acquired and an efficient and practical model can be established by the methods. In addition, the controller designed and applied in this paper has good performance and small steady-state error, which can meet the basic flight mission requirements, including stability of flight attitude, path following and switching of different waypoints. These modeling and control methods can also be employed in other small battery-powered fixed-wing UAV projects.