Development and Flight Test of Fixed-Wing UAV using Geometry Definition with GUI and Panel Method Analysis

Flight testing full conversion of a 40-kg-class tilt-duct unmanned aerial vehicle

The development of solar-powered high-altitude UAV has gained increasing attention in the recent years. Several aircraft have had successful flights in the stratosphere, but despite advances in lightweight design they can only carry small payloads compared to the total takeoff mass. This paper suggests to eliminate the need for a landing gear by landing on a mobile ground vehicle. This would not only increase the payload capacity, but also simplify landings in crosswind conditions and thus increase the operational availability. A system with a small UAV and a car-mounted landing platform is prepared as a technology demonstrator. Different aspects of the landing problem are studied in simulations and real experiments and algorithms for the cooperative control of both vehicles are proposed. Simulations as well as experiments with the real car and a simulated UAV show the feasibility of such landings.

This paper presents a modeling and simulation tool for the design and development of a Quad-Rotor UAV, which is capable of indoor flight with the aid of MEMS-based sensors and ultrasonic rangefinders. The vehicle requires an attitude stabilizing controller to allow it to be flown indoors without a high degree of operating skill. Attitude estimations are difficult without GPS aiding and expensive inertial sensors. An Extended Kalman Filter (EKF) is proposed to estimate the attitude states with the aid of rangefinders, but the system must be thoroughly tested in simulation. The paper presents a dynamics model and simulation tool for design and validation of the controller and state estimator prior to flight test. The simulation is performed under Mathworks’ Matlab/Simulink environment. A method for real-time simulation and visualization is also developed.

Flight Test Driven Development of Low Cost UAVs - Pitfalls and Opportunities

Unmanned aerial vehicles (UAV) are useful for many applications where human intervention is considered difficult or dangerous. Traditionally, the fixed-wing UAV has been served as the unit for these dangerous tasks because the control is easy. Rotorcraft UAV (RUAV), on the other hand, can operate in many different flight modes which the fixed-wing one is unable to achieve, such as vertical take-off/landing, hovering, lateral flight, pirouette, and bank-to-turn. Due to the versatility in maneuverability, helicopters are capable to fly in and out of restricted areas and hover efficiently for long periods of time. These characteristics make RUAV applicable for many military and civil applications. However, the control of RUAV is difficult. Although some control algorithms have been proposed (Sanders et al., 1998, Garratt et al., 2003, Enns et al., 2000, Bijnens et al., 2005, Koo et al., 1998, Jiang et al., 2006), most of them were verified by simulation instead of real experiments. One reason for this is due to the complicate, nonlinear and inherently unstable dynamics, which has cross coupling between main and tail rotor, and lots of time-varying aerodynamic parameters. Another reason is that the flight test is in high risk. If a RUAV lost its control, it would never be stabilized. Shenyang Institute of Automation, Chinese Academy of Sciences (SIA, CAS) as a national research institute focus its future research on RUAV 5 years ago. Until now, we have 3 types of experimental platforms for advanced control algorithm research demonstration. ServoHeli-20 (Fig.1) is a model class platform which has 20 kilograms takeoff weight. ServoHeli-40 (Fig.2) and ServoHeli-110 (Fig.3) are engineering class platforms for highway patrol, electrical line patrol and photography. They have 40 and 110 kilograms takeoff weight and have finished full autonomous flight control experimental demonstration. In SIA, the control algorithm research on RUAV involves in navigation, advanced flight control, 3D path planning and fault tolerant control. This paper details the development of an unmanned helicopter testbed – ServoHeli-20 (Qi et al., 2006) (Fig.3), and the experiments performed toward achieving full autonomous flight. The brief of this paper is as follow: the ServoHeli-20 platform is introduced in Section II. The introduction of sensor package is in Section III. The modeling of the RUAV system is presented in the Section IV. In Section V, we introduce an independent-channel control scheme as a baseline control of the platform. In Section VI, an overview of fault tolerant

This paper presents an obstacle avoidance algorithm that avoids 2D circular objects during an autonomous waypoint navigation flight of a fixed-wing UAV. We named the obstacle avoidance algorithm the Pre-Determined Virtual Circular Obstacle Avoidance Algorithm (PDVCOAA). If a circular obstacle is situated in between two consecutive target waypoints, the PDVCOAA generates an updated trajectory with new target waypoints to avoid the obstacle(s). The new target waypoint navigation plan is uploaded from a ground station to the flight controller on the UAV. The ground station software used in this work is QGroundControl and the flight controller is the Pixhawk 4 Mini, with PX4-Autopilot as its firmware. The performance of the PDVCOAA obstacle avoidance algorithm is assessed using a sample flight plan shared by the organizers of the 2022-23 SUAS competition through Software In The Loop (SITL) testing with a simulated fixed-wing vehicle as well as several custom flight plans in the Gazebo simulator. Once the obstacle avoidance algorithm was validated in the simulation environment, it was flight tested on a custom designed fixed-wing UAV. From the flight tests, we’ve validated that our UAV can avoid obstacles with a radius of 150 ft. and an acceptance target waypoint radius 33.2 ft (default setting in PX4 firmware) and 25 ft (acceptance radius specified in 2022-23 SUAS competition rules).

Aerodynamic interactions between the rotor and the empennage can have a significant impact on steady and unsteady loads and often result in challenges in a rotorcraft design phase. In the present work, numerical analysis of rotor-empennage aerodynamic interactions were compared to full-scale flight test data with respect to steady and unsteady interactional aerodynamic effects. The flight tests provided loads for a low-empennage and a T-Tail configuration for various forward flight velocities. For the T-Tail configuration, additional pressure sensors provided validation data for steady and unsteady interaction effects. The numerical analysis was focused on an unsteady panel method, complemented by high-fidelity CFD/CSM-coupling results for a level flight state. Furthermore, a supplemental validation of the unsteady panel method was performed against an isolated wing-vortex interaction experiment. The flight test data revealed a strong asymmetry in mean empennage loads, which increases with forward flight velocity. The numerical analysis showed coherent results with a slight over-prediction in high-speed. The T-Tail configuration is furthermore subject to 3D effects between the vertical- and horizontal tail. These effects influence pressure and load-distributions on the T-Tail, which was captured by both numerical methods. The general characteristics of the unsteady pressure signatures were captured by both methods. The panel method showed slightly better representation of amplitude.

A numerical simulation process based on loosely coupling a helicopter flight mechanics code and a 3D panel and free wake aerodynamics method is extended for arbitrary steady flight states and quasi-steady maneuver analysis. First, the basic coupling procedure and their building blocks are introduced. Then, the extensions for arbitrary steady flight states are illustrated and a newly developed procedure for approximate maneuver simulations using a sequence of steady aeromechanically coupled simulations is presented. The aim of this procedure is to provide a rapid method for evaluation of loads, performance and stability in the helicopter design and development process. It includes an improved physics-based modeling of aerodynamic interactions as compared to low-fidelity aerodynamics models used in comprehensive codes. At the same time the computational effort is dramatically reduced as compared to highf idelity aerodynamics methods. Finally, validation results for the coupling procedure and first applications of the maneuver simulation process are shown. The computed results are compared with flight tests and blade element momentum theory based results. In many cases the solution accuracy can be significantly improved by using the coupled simulation procedure. But for maneuvers involving fast pilot inputs or considerable translational or rotational accelerations of the helicopter, the quasi-steady coupling approach is not suitable and an extension to fully unsteady loose coupling or the implementation of a tight coupling approach is proposed.

Design, analysis and test of conventional and winglet propellers under highly loaded conditions

This study describes a novel method for estimating the strength and direction of the local wind field from a mobile airborne platform. An iterative optimisation is derived that allows the properties of the wind field to be determined from successive measurements of the heading direction and ground track of the aircraft only. We have previously described methods for estimating these parameters using a single vision system. This approach therefore constitutes a purely visual method for estimating the properties of the local wind field. We present results from simulated and real-world flight tests that demonstrate the accuracy and robustness of the proposed method and its practicality in uncontrolled environmental conditions. These properties and the simplicity of the implementation should make this approach useful as an alternative means for estimating the properties of the local wind field from small-scale, fixed-wing UAVs.

This paper discusses the trajectory tracking problem for miniature fixed-wing UAVs subject to wind gust disturbances and heterogeneous uncertainties. Based on a simplified aerodynamic model, a 6-state translational model is derived from the fixed-wing UAV dynamics by assuming an ideal inner-loop attitude controller. Combining signal compensation and feedback linearization method, we propose a new robust trajectory tracking controller for planar motion control, which incorporates a nominal linear controller to achieve the prescribed convergence and a robust compensator to restrain the effect of disturbances and uncertainties, while a well-tuned PID controller is developed for altitude regulation. The proposed robust control scheme is implemented on a miniature fixed-wing UAV and the flight tests for different scenarios(including both strong and light wind) are performed. The experimental results are shown to demonstrate the effectiveness and robustness of the robust control scheme.

In this paper, a novel data-driven control (DDC) algorithm is proposed to adaptive control for fixed-wing (Unmanned Aerial Vehicle) UAV path following using input and output (I/O) data. Differently from model based control, data-driven control reduces the dependence of accurate mathematics model in process of controller designation. The key difficulty in fixed-wing UAV path following is that the model of UAV is generally underactuated and nonlinear, and easy to be affected by disturbances. The unique feature of this paper is that we explicitly employ data-driven control to design controller directly in two dimensions at constant height to overcome the drawback of dynamical uncertainties and wind disturbances. The tracking error is guaranteed converging to zero and simulation results and flight test show the effectiveness and applicability of the control law.

Frequency domain identification of a fixed-wing UAV using flight test data is presented. Based on the identified model, handling qualities are evaluated. The instrumentation and experiment design are also presented and discussed. The flight test data used in this study are obtained from open loop flights of the UAV. The identification of frequency response and state-space models for the longitudinal and lateral/directional dynamics is accomplished using the frequency-domain identification methods of CIFER ® .

Incremental nonlinear dynamic inversion shows good robustness and low dependence on the model information. In this study, incremental nonlinear dynamic inversion is improved and utilized to solve the six-degrees-of-freedom waypoint tracking problem of fixed-wing unmanned aerial vehicles (UAVs). This research is focused on studying the acquisition of the state derivatives and the processing method of the derivative time delay. A nondelay differentiator design method and an incremental gain method that responds to time delay are developed in this study. Additionally, a universal waypoint navigation method based on incremental nonlinear dynamic inversion is put forward to enable the aircraft to successfully reach its waypoint from any spatial position and under any motion states. Finally, the proposed improvements of the incremental nonlinear dynamic inversion are verified through flight tests.

The use of unmanned aerial vehicles (UAVs) that can operate autonomously in dynamic and dangerous operational environments are becoming increasingly common. In such operations, object detection, classification and tracking can often be one of the main goals. In recent years there has been an increased focus on embedded hardware that is both small and powerful, making UAV on-board data processing more viable. Being able to process the video feed on-board the UAV calls for fast and robust real-time algorithms for object identification and tracking. This paper discusses the development and implementation of a machine vision system for a low-cost fixed-wing UAV with a total flying weight of less than 4kg. The machine vision system incorporates the use of a thermal imaging camera and on-board processing power to perform real-time object detection, classification and tracking of objects in the ocean surface. The system is tested on thermal video data from a test flight, and is found to be able to detect 99;6% of objects of interest located in the ocean surface. Of the detected objects, only 5% were false positives. Furthermore, it classifies 93; 3% of the object types it is trained to classify correctly. The classifier is highly agile, allowing the user to quickly define which object characteristics that should be considered during classification, and what types of objects to classify. Finally, the system is found to successfully track 85% of the object types it is actively searching for in a real-time simulation test.