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The purpose of this paper is to present an approach to a polar graph measurement by a flight testing technique and to propose a baseline research method for future tests of UAV polar graphs. The method presented can be used to demonstrate a conceptual and preliminary design process using a scaled, unmanned configuration. This shows how results of experimental flight tests using a scaled flying airframe may be described and analysed before manufacturing the full scale aircraft.

During the research, the flight tests were conducted for two aerodynamic configurations of a small UAV. This allowed the investigation of the influence of winglets and classic vertical stabilizers on the platform stability, performance and therefore polar graphs of a small unmanned aircraft.

A methodology of flight tests for the assessment of a small UAV’s polar graph has been proposed, performed and assessed. Two aerodynamic configurations were tested, and it was found that directional stability had a large influence on the UAV’s performance. A correlation between the speed and inclination of the altitude graph was found – i.e. the higher the flight speed, the steeper the altitude graph (higher descent speed, steeper flight path angle). This could be considered as a basic verification that the recorded data have a physical sense.

The polar graph and therefore glide ratio of the aircraft is a major factor for determining its performance and power required for flight. Using the right flight test procedure can speed-up the process of measuring glide ratio, making it easier, faster, robust, more effective and accurate in future research of novel, especially unorthodox configurations. This paper also can be useful for the proper selection of requirements and preliminary design parameters for making the design process more economically effective.

This paper presents a very efficient method of assessing the design parameters of UAVs, especially the polar graph, in an early stage of the design process. Aircraft designers and producers have been widely performing flight testing for years. However, these procedures and practical customs are usually not wide spread and very often are treated as the company’s “know how”. Results presented in this paper are original, relatively easily be repeated and checked. They may be used either by professionals, highly motivated individuals and representatives of small companies or also by ambitious amateurs.

The purpose of this paper is to present a novel approach based on the differential search (DS) algorithm integrated with the adaptive network-based fuzzy inference system (ANFIS) for unmanned aerial vehicle (UAV) winglet design.

The winglet design of UAV, which was produced at Faculty of Aeronautics and Astronautics in Erciyes University, was redesigned using artificial intelligence techniques. This approach proposed for winglet redesign is based on the integration of ANFIS into the DS algorithm. For this purpose, the cant angle (c), the twist angle (t) and taper ratio (λ) of winglet are selected as input parameters; the maximum value of lift/drag ratio (E_max) is selected as the output parameter for ANFIS. For the selected input and output parameters, the optimum ANFIS parameters are determined by the DS algorithm. Then the objective function based on optimum ANFIS structure is integrated with the DS algorithm. With this integration, the input parameters for the E_max value are obtained by the DS algorithm. That is, the DS algorithm is used to obtain both the optimization of the ANFIS structure and the necessary parameters for the winglet design. Thus, the UAV was reshaped and the maximum value of lift/drag ratio was calculated based on new design.

Considerable improvements on the max E are obtained through winglet redesign on morphing UAVs with artificial intelligence techniques.

It takes a long time to obtain the optimum E_max value by the computational fluid dynamics method.

Using artificial intelligence techniques saves time and reduces cost in maximizing E_max value. The simulation results showed that satisfactory E_max values were obtained, and an optimum winglet design was achieved. Thus, the presented method based on ANFIS and DS algorithm is faster and simpler.

The application of artificial intelligence methods could be used in designing more efficient aircrafts.

The study provides a new and efficient method that saves time and reduces cost in redesigning UAV winglets.

Unmanned aerial vehicles (UAV) with remote and/or automated flight and mission controls have replaced airplanes with pilots in many important roles. This study aims to deal with computational fluid dynamics (CFD) analysis and development of the aerodynamic configuration of a multi-purpose UAV for low and medium altitudes. The main aerodynamic requirement was the application of the tandem wing (TW) concept, where both wings generate a positive lift and act as primary lifting devices.

Initial design analyses of the UAV’s aerodynamic configuration were performed using ANSYS Fluent. In previous work in Fluent, the authors established a calculation model that has been verified by experiments and, with minor adjustments, could be applied for subsonic, transonic and supersonic flow analyses.

The design evolved through eight development configurations, where the latest V8 satisfied all the posted longitudinal aerodynamic requirements. Both wings generate a substantial amount of positive lift, whereas the initial stall occurs first on the front wing, generating a natural nose-down stall recovery tendency. In the cruising flight regime, this configuration has the desired range of longitudinal static stability and its centre of pressure is in close proximity to the centre of gravity.

The intermediate development version V8 with proper longitudinal aerodynamic characteristics presents a good starting point for future development steps that will involve the optimization of lateral-directional aerodynamics.

Using contemporary CFD tools, a novel and original TW aerodynamic configuration have evolved within eight development stages, not being based on or derived from any existing designs.

The presented research is carried out in reaction to the soaring costs of fuel and tight control over environmental issues such as carbon dioxide emissions and noise. The purpose of this paper is to study the feasibility of applying the environmental-friendly energy source in an unmanned aerial vehicles (UAVs) propulsion system.

Currently, the majority of UAVs are still powered by conventional combustion engines. An electric propulsion system is most commonly found in civilian micro and mini UAVs. The UAV classification is reviewed in this study. This paper focuses mainly on application of electric propulsion systems in UAVs. Investigated hybrid energy systems consist of fuel cells, Li-ion batteries, super-capacitors and photovoltaic (PV) modules. Current applications of fuel cell systems in UAVs are also presented.

The conducted research shows that hybridization allows for better energy management and operation of every energy source onboard the UAV within its limits. The hybrid energy system design should be created to maximize system efficiency without compromising the performance of the aircraft.

The presented study highlights the reduction of the energy consumption, necessary to perform the mission and maximizing of the endurance with simultaneous decrease in emissions and noise level.

The conducted research studies the feasibility of implementing the environmental-friendly hybrid electric propulsion systems in UAVs that offers high efficiency, reliability, controllability, lack of thermal and noise signature, thus, providing quiet and clean drive with low vibration levels. This paper highlights the main challenges and current research on fuel cell in aviation and draws attention to fuel cell – electric system modeling, hybridization and energy management.

A distinctive feature of tilt-rotor UAVs is that they can be fully actuated, whereas in fixed-angle rotor UAVs (e.g. common-type quadrotors, octorotors, etc.), the associated dynamic model is characterized by underactuation. Because of the existence of more control inputs, in tilt-rotor UAVs, there is more flexibility in the solution of the associated nonlinear control problem. On the other side, the dynamic model of the tilt-rotor UAVs remains nonlinear and multivariable and this imposes difficulty in the drone's controller design. This paper aims to achieve simultaneously precise tracking of trajectories and minimization of energy dissipation by the UAV's rotors. To this end elaborated control methods have to be developed.

A solution of the nonlinear control problem of tilt-rotor UAVs is attempted using a novel nonlinear optimal control method. This method is characterized by computational simplicity, clear implementation stages and proven global stability properties. At the first stage, approximate linearization is performed on the dynamic model of the tilt-rotor UAV with the use of first-order Taylor series expansion and through the computation of the system's Jacobian matrices. This linearization process is carried out at each sampling instance, around a temporary operating point which is defined by the present value of the tilt-rotor UAV's state vector and by the last sampled value of the control inputs vector. At the second stage, an H-infinity stabilizing controller is designed for the approximately linearized model of the tilt-rotor UAV. To find the feedback gains of the controller, an algebraic Riccati equation is repetitively solved, at each time-step of the control method. Lyapunov stability analysis is used to prove the global stability properties of the control scheme. Moreover, the H-infinity Kalman filter is used as a robust observer so as to enable state estimation-based control. The paper's nonlinear optimal control approach achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs. Finally, the nonlinear optimal control approach for UAVs with tilting rotors is compared against flatness-based control in successive loops, with the latter method to be also exhibiting satisfactory performance.

So far, nonlinear model predictive control (NMPC) methods have been of questionable performance in treating the nonlinear optimal control problem for tilt-rotor UAVs because NMPC's convergence to optimum depends often on the empirical selection of parameters while also lacking a global stability proof. In the present paper, a novel nonlinear optimal control method is proposed for solving the nonlinear optimal control problem of tilt rotor UAVs. Firstly, by following the assumption of small tilting angles, the state-space model of the UAV is formulated and conditions of differential flatness are given about it. Next, to implement the nonlinear optimal control method, the dynamic model of the tilt-rotor UAV undergoes approximate linearization at each sampling instance around a temporary operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. The linearization process is based on first-order Taylor series expansion and on the computation of the associated Jacobian matrices. The modelling error, which is due to the truncation of higher-order terms from the Taylor series, is considered to be a perturbation that is asymptotically compensated by the robustness of the control scheme. For the linearized model of the UAV, an H-infinity stabilizing feedback controller is designed. To select the feedback gains of the H-infinity controller, an algebraic Riccati equation has to be repetitively solved at each time-step of the control method. The stability properties of the control scheme are analysed with the Lyapunov method.

There are no research limitations in the nonlinear optimal control method for tilt-rotor UAVs. The proposed nonlinear optimal control method achieves fast and accurate tracking of setpoints by all state variables of the tilt-rotor UAV under moderate variations of the control inputs. Compared to past approaches for treating the nonlinear optimal (H-infinity) control problem, the paper's approach is applicable also to dynamical systems which have a non-constant control inputs gain matrix. Furthermore, it uses a new Riccati equation to compute the controller's gains and follows a novel Lyapunov analysis to prove global stability for the control loop.

There are no practical implications in the application of the nonlinear optimal control method for tilt-rotor UAVs. On the contrary, the nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations (SDRE). The SDRE approaches can be applied only to dynamical systems which can be transformed to the linear parameter varying (LPV) form. Besides, the nonlinear optimal control method performs better than nonlinear optimal control schemes which use approximation of the solution of the Hamilton–Jacobi–Bellman equation by Galerkin series expansions. The stability properties of the Galerkin series expansion-based optimal control approaches are still unproven.

The proposed nonlinear optimal control method is suitable for using in various types of robots, including robotic manipulators and autonomous vehicles. By treating nonlinear control problems for complicated robotic systems, the proposed nonlinear optimal control method can have a positive impact towards economic development. So far the method has been used successfully in (1) industrial robotics: robotic manipulators and networked robotic systems. One can note applications to fully actuated robotic manipulators, redundant manipulators, underactuated manipulators, cranes and load handling systems, time-delayed robotic systems, closed kinematic chain manipulators, flexible-link manipulators and micromanipulators and (2) transportation systems: autonomous vehicles and mobile robots. Besides, one can note applications to two-wheel and unicycle-type vehicles, four-wheel drive vehicles, four-wheel steering vehicles, articulated vehicles, truck and trailer systems, unmanned aerial vehicles, unmanned surface vessels, autonomous underwater vessels and underactuated vessels.

The proposed nonlinear optimal control method is a novel and genuine result and is used for the first time in the dynamic model of tilt-rotor UAVs. The nonlinear optimal control approach exhibits advantages against other control schemes one could have considered for the tilt-rotor UAV dynamics. For instance, (1) compared to the global linearization-based control schemes (such as Lie algebra-based control or flatness-based control), it does not require complicated changes of state variables (diffeomorphisms) and transformation of the system's state-space description. Consequently, it also avoids inverse transformations which may come against singularity problems, (2) compared to NMPC, the proposed nonlinear optimal control method is of proven global stability and the convergence of its iterative search for an optimum does not depend on initialization and controller's parametrization, (3) compared to sliding-mode control and backstepping control the application of the nonlinear optimal control method is not constrained into dynamical systems of a specific state-space form. It is known that unless the controlled system is found in the input–output linearized form, the definition of the associated sliding surfaces is an empirical procedure. Besides, unless the controlled system is found in the backstepping integral (triangular) form, the application of backstepping control is not possible, (4) compared to PID control, the nonlinear optimal control method is of proven global stability and its performance is not dependent on heuristics-based selection of parameters of the controller and (5) compared to multiple-model-based optimal control, the nonlinear optimal control method requires the computation of only one linearization point and the solution of only one Riccati equation.

This paper aims to tele-operate the movement of an unmanned aerial vehicle (UAV) in the obstructed environment with asymmetric time-varying delays. A simple passive proportional velocity errors plus damping injection (P-like) controller is proposed to deal with the asymmetric time-varying delays in the aerial teleoperation system.

This paper presents both theoretical and real-time experimental results of the bilateral teleoperation system of a UAV for collision avoidance over the wireless network. First, a position-velocity workspace mapping is used to solve the master-slave kinematic/dynamic dissimilarity. Second, a P-like controller is proposed to ensure the stability of the time-delayed bilateral teleoperation system with asymmetric time-varying delays. The stability is analyzed by the Lyapunov–Krasovskii function and the delay-dependent stability criteria are obtained under linear-matrix-inequalities conditions. Third, a vision-based localization is presented to calibrate the UAV’s pose and provide the relative distance for obstacle avoidance with a high accuracy. Finally, the performance of the teleoperation scheme is evaluated by both human-in-the-loop simulations and real-time experiments where a single UAV flies through the obstructed environment.

Experimental results demonstrate that the teleoperation system can maintain passivity and collision avoidance can be achieved with a high accuracy for asymmetric time-varying delays. Moreover, the operator could tele-sense the force reflection to improve the maneuverability in the aerial teleoperation.

A real-time bilateral teleoperation system of a UAV for collision avoidance is performed in the laboratory. A force and visual interface is designed to provide force and visual feedback of the slave environment to the operator.

The purpose of this paper is to present the development of hardware‐in‐the‐loop simulation (HILS) for visual target tracking of an octorotor unmanned aerial vehicle (UAV) with onboard computer vision.

HILS for visual target tracking of an octorotor UAV is developed by integrating real embedded computer vision hardware and camera to software simulation of the UAV dynamics, flight control and navigation systems run on Simulink. Visualization of the visual target tracking is developed using FlightGear. The computer vision system is used to recognize and track a moving target using feature correlation between captured scene images and object images stored in the database. Features of the captured images are extracted using speed‐up robust feature (SURF) algorithm, and subsequently matched with features extracted from object image using fast library for approximate nearest neighbor (FLANN) algorithm. Kalman filter is applied to predict the position of the moving target on image plane. The integrated HILS environment is developed to allow real‐time testing and evaluation of onboard embedded computer vision for UAV's visual target tracking.

Utilization of HILS is found to be useful in evaluating functionality and performance of the real machine vision software and hardware prior to its operation in a flight test. Integrating computer vision with UAV enables the construction of an unmanned system with the capability of tracking a moving object.

HILS for visual target tracking of UAV described in this paper could be applied in practice to minimize trial and error in various parameters tuning of the machine vision algorithm as well as of the autopilot and navigation system. It also could reduce development costs, in addition to reducing the risk of crashing the UAV in a flight test.

A HILS integrated environment for octorotor UAV's visual target tracking for real‐time testing and evaluation of onboard computer vision is proposed. Another contribution involves implementation of SURF, FLANN, and Kalman filter algorithms on an onboard embedded PC and its integration with navigation and flight control systems which enables the UAV to track a moving object.

The purpose of this paper is to present a novel approach based on the artificial bee colony (ABC) algorithm aiming to achieve maximum acceleration and maximum endurance for morphing unmanned aerial vehicle (UAV) design.

Some of the most important issues in the design of UAV are the design of thrust system and determination of the endurance of the UAV. Although propeller selection is very important for the thrust system design, battery selection has the utmost importance for the determination of UAV endurance. In this study, the calculations of maximum acceleration and endurance required by ZANKA-II during the flight are considered simultaneously. For this purpose, a model based on the ABC algorithm is proposed for the morphing UAV design, aiming to achieve the maximum acceleration and endurance. In the proposed model, the propeller diameter, propeller pitch and battery values used in morphing UAV's power system design are selected as the input parameters; maximum acceleration and endurance are selected as the output parameters. To obtain the maximum acceleration and endurance, the optimum input parameters are determined through the ABC algorithm-based model.

Considerable improvements on maximum acceleration and endurance of morphing UAV with ABC algorithm-based model are obtained.

The endurance and acceleration due to the thrust are two separate parameters that are not normally proportional to each other. In this study, optimization of UAV’s endurance and acceleration is considered with equal importance.

Using artificial intelligence techniques causes fast and simple optimization for determination of UAV’s endurance and acceleration with equal importance. In the simulation studies with ABC algorithm, satisfactory results are obtained.

The results of the study have showed that the proposed approach could be an alternative method for UAV designers.

Providing a new and efficient method saves time and reduces cost in calculations of maximum acceleration and endurance of the UAV.

The paper aims to investigate the compatibility of manned-aircraft airborne collision avoidance systems (ACAS) for use on unmanned aerial vehicles (UAV).

The paper uses the Fault Tree method for defining ACAS model adapted for the UAV operations, with the aim of showing the presence of certain factors that configure in such ACAS system, and whose failure can lead to an adverse event – mid-air collision.

Based on the effectiveness analysis of ACAS solution adapted for the UAV operations, for given inputs, it can be concluded that the probability of ACAS failure is on the order of 10–4, as well as that in the case of autonomous ACAS solution for the UAV, the probability is reduced to 10–5. The most influential factors for the failure of the UAV’s ACAS are as follows: technical implications on the UAV, human factor, sensor error, communication and C2 link issue.

The established model could be used both in the UAV’s ACAS design and application phases, with the aim of assessing the effectiveness of the adopted solution. The model outputs not only highlight the critical points of the system but also provide the basis for defining the Target Level of Safety (TLOS) for the UAV operations.

The developed model can be expected to speed up the design and adoption process of ACAS solutions for the UAVs. Also, the paper presents one of the first attempts to quantify TLOS for the UAV operations in the context of collision avoidance systems.

The purpose of this paper is to develop a distributed and integrated method to get a fast and feasible solution for cooperative mission planning of multiple heterogeneous unmanned aerial vehicles (UAVs).

In this study, the planning process is conducted in a distributed framework; the cooperative mission planning problem is reformulated with some specific constraints in the real mission; a distributed genetic algorithm is the algorithm proposed for searching for the optimal solution; genes of the chromosome are modified to adapt to the heterogeneous characteristic of UAVs; a fixed-wing UAV’s six degrees-of-freedom (DOF) model with a path following method is used to test the proposed mission planning method.

This method not only has the ability to obtain good feasible solutions but also improves the operating rate vastly.

This study is only applied to the case where the communication among UAVs is linked during the mission.

This study is expected to be practical for a real mission because of its fast operating rate and good feasible solution.

This solution is tested on a fixed-wing UAV’s 6-DOF model by a path following method, so it is believable from the perspective of an autonomous UAV guidance and control system.