Fixed-wing Unmanned Aerial Vehicle Research Articles

Design, Construction, and Flight Performance of an Electrically Operated Fixed-Wing UAV

The development of unmanned aerial vehicles (UAVs) has attracted much attention in the global community and aviation industry. As UAVs have the potential to be applied for multiple missions, the level of research into improving their design and flight performance has also increased. In this context, the present paper aims to present the design, construction, and flight performance of an electrically operated fixed-wing UAV. As a first step in the design process, key performance requirements are defined, such as the thrust required, the stall speed, the minimum drag velocity, and the minimum power velocity. Wing and associated power loadings are calculated according to the defined performance requirements. In addition, payload and endurance requirements are set up in order to determine the wing and tail areas, the total mass, the power requirements, and the motor size. Aerodynamics and stability designs are also calculated. After the completion of the design process, the manufacturing of the UAV follows by using appropriate materials. Flight tests were carried out for the evaluation of the UAV’s flight performance, where the success of the design was demonstrated.

Design and Control of an Ultra-Low-Cost Logistic Delivery Fixed-Wing UAV

In contemporary logistics, the deployment of fixed-wing unmanned aerial vehicles (UAVs) as a transportation platform is experiencing rapid advancements, garnering substantial application within numerous logistic operations with pronounced efficacies. There are notable impediments to the utilization of commercial logistic-oriented fixed-wing UAVs, including elevated procurement and maintenance costs, extensive maintenance intervals, and unsuitability for small-volume, low-altitude transport tasks. These factors collectively exacerbate the risk associated with enterprise procurement and elevate the cost–benefit ratio. This study introduces the design and fabrication of a cost-efficient UAV for logistic delivery purposes, constructed primarily from cost-effective wood materials. This UAV is engineered to ferry payloads of up to 1000 g across a predefined aerial route at an altitude of 40 m. Upon reaching the designated location, the UAV is programmed to initiate the identification of the drop zone, thereafter descending to facilitate the release of the cargo. To mitigate the impact force during the landing phase, the payload was encapsulated within a sponge-damping layer, thereby preserving the integrity of the transported items. The empirical findings from outdoor delivery trials underscore the UAV’s ability to precisely execute payload drops at the targeted locations, confirming its potential to fulfill the logistical requirements for the transportation and delivery of small-volume items in a cost-effective, low-altitude framework. This investigation contributes to the burgeoning discourse on leveraging ultra-low-cost UAVs in logistics, offering a feasible solution to the challenges of cost and efficiency in UAV-operated delivery systems.

Extended state observer-based continuous finite time control for a fixed-wing vertical take-off and landing unmanned aerial vehicle

Dual system Vertical Take-Off and Landing (VTOL) fixed-wing Unmanned Aerial Vehicles (UAVs) combine the advantages of both the rotorcraft and fixed-wing UAV, characterizing horizontal and vertical flight capabilities. However, they face challenges such as aerodynamic coupling between propulsion systems, model uncertainty, and external disturbances, significantly deteriorating the tracking accuracy and robustness. This paper proposes an Extended State Observer based Continuous Finite-Time Control (ESO-CFTC) scheme to solve the attitude and trajectory tracking problem of such a VTOL fixed-wing UAV. Initially, the Nonsingular Fast Terminal Sliding Mode (NFTSM) controller guarantees the tracking errors reach a prescribed manifold in finite time with a continuous reaching law, then converge to the origin in finite time while avoiding singularities. To suppress the inherent chattering problem of the sliding mode control, an ESO technique is incorporated into the ESO-CFTC scheme, which is completely decoupled with the basic NFTSM controller. The supremum of the estimation residual set is derivated theoretically. The scheme forces the tracking errors to converge into the neighborhood of the origin in finite time without requiring prior knowledge of lumped disturbance supremum values. Finally, the numerical simulation results in a Matlab/Simulink environment demonstrate that the presented method outperforms existing benchmark in terms of disturbance estimation, tracking accuracy, convergence rate, and robustness.

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.

A Two-Layer Communication Relay Planning Method for a Fixed-Wing UAVs Swarm

A Two-Layer Communication Relay Planning Method for a Fixed-Wing UAVs Swarm

Trajectory Tracking Control for Fixed-Wing UAV Based on DDPG

Trajectory Tracking Control for Fixed-Wing UAV Based on DDPG

Maximisation the autonomous flight performance of unmanned helicopter using BSO algorithm

Abstract The usage areas of rotary or fixed wing unmanned aerial vehicles (UAV) have become very widespread with technological developments. For this reason, UAV designs differ in terms of aerodynamic design, flight performance and endurance depending on the intended use. In this study, maximising of the autonomous flight performance of the unmanned helicopter produced at Erciyes University using an optimisation algorithm is discussed. For this purpose, the input parameters of the dynamic model are chosen as blade length, blade mass density, blade chord width and blade twist angle of the unmanned helicopter and the proportional, integral, derivative gain coefficients of the lateral axis of the autopilot. The output parameters of the dynamic model are selected as settling time, rise time and maximum overshoot, which are autonomous performance parameters. The dynamic model consisting of helicopter and autopilot parameters is integrated into the back-tracking search optimisation (BSO) algorithm as an objective function. In the optimization process, where mean squared error (MSE) is used as the performance criterion, optimum input and output values were determined. Thus, helicopter and autopilot parameters, which are among the factors affecting autonomous performance, are taken into account with equal importance and simultaneously. Simulations show that the obtained values are satisfactory. With this approach based on the optimisation method, complex and time-consuming dynamic model calculations are reduced, time and cost are saved, and practicality is achieved in applications. Therefore, this approach can be an innovative and alternative method to improve UAV designs and increase flight performance compared to classical methods.

FW-UAV fault diagnosis based on knowledge complementary network under small sample

Fixed Wing Unmanned Aerial Vehicles (FW-UAVs) are prone to faults when performing a variety of tasks, which can lead to mission failure and even pose a safety risk. These faults can be recorded by mission-specific time-series flight data, but are very limited. Traditional methods are usually difficult to process these data, which poses a huge challenge to FW-UAV fault diagnosis (FD). To address this problem, this paper proposed a novel Heterogeneous Deep Multi-Task Learning (HDMTL) framework with adaptive sharing and knowledge complementation for FW-UAV FD. Specifically, we first capture the temporal and spatial features in the flight data through sub-networks respectively. Then, we design a novel attention-based adaptive sharing strategy. The sharing strategy aims to transfer relevant knowledge to different sub-networks to improve their prediction accuracy through knowledge complementation. Finally, extensive experimental results show that HDMTL is significantly competitive with currently popular methods. The code and data are available at https://github.com/YizongZhang/HDMTL.

Interpretable intelligent fault diagnosis strategy for fixed-wing UAV elevator fault diagnosis based on improved cross entropy loss

The current popular machine learning-based fault diagnosis methods make it difficult to explain the diagnostic results, leading to low user trust in such diagnostic techniques. In this regard, this paper explores the study of the interpretability of intelligent fault diagnosis algorithms using the elevator of a fixed-wing unmanned aerial vehicle (UAV) as a diagnostic object. The Transformer model combines excellent modeling capability and efficient sequence data processing, is chosen to mine fault signal features to guarantee accurate diagnosis. Among the proposed interpretable fault diagnosis models, the local interpretable model-agnostic explanations (LIME) model is used to provide explicit interpretability for the decision-making process of the diagnosis model. In addition, a loss function called RDCE (reinforced diagnostic cross-entropy) Loss is designed to minimize the negative impact of different sample sizes for different fault types on the diagnostic performance. This loss function is designed to weigh the various types of faults to speed up the convergence of the model and improve the diagnostic accuracy. By comparing the proposed diagnostic strategy with other commonly used machine learning models, including long short term memory and recurrent neural network (RNN), the average diagnostic accuracy of the proposed diagnostic strategy is 99.97%, significantly better than that of the comparison algorithms. At the same time, this paper provides an in-depth interpretable analysis of the diagnostic process of the Transformer. The diagnostic process of the Transformer model gives the reasons for the diagnostic results from the point of view of the kind of features processed by the model. Based on this, the diagnostic model is simplified. After streamlining the number of features from 40 to 24 according to their importance, the diagnostic accuracy of the model is improved by 0.26%, and the diagnostic efficiency is improved. In addition, the proposed diagnostic strategy also shows significant advantages in terms of noise robustness.

Innovative Hybrid UAV Design, Development, and Manufacture for Forest Preservation and Acoustic Surveillance

The research described in this paper focuses on the development of an innovative unmanned aerial vehicle (UAV) tailored for a specific mission: detecting the acoustic signature emitted by chainsaws, identifying deforestation, and reporting its location for legality assessment. Various calculations were conducted to determine the optimal solution, resulting in the choice of a fixed-wing UAV. A comparative analysis between tri-rotor and quadcopter systems was performed, leading to the selection of the tri-rotor configuration. The primary objective of this study is to design an innovative hybrid UAV concept with key features including a fixed-wing design and integrated VTOL (vertical takeoff and landing) capability in the experimental model. The aircraft has been constructed using advanced materials such as fiber-reinforced polymer composites, manufactured using both conventional and advanced techniques like continuous fiber additive manufacturing and the use of a polymer matrix. Additionally, the aerodynamic configuration is optimized to achieve a cruise speed of approximately 50 km/h and a flight autonomy exceeding 3 h. The UAV has been equipped with payloads for mounting sensors to collect meteorological data, and crucially, the VTOL system has been optimized to vectorize thrust for improved performance during the transition from hover to cruise flight. This paper details the entire manufacturing and assembly process of the drone, covering both the structural framework and associated electrical installations. A dedicated sound detection system is incorporated into the drone to identify chainsaw noise, with the aim of preventing deforestation.

Online Predictive Visual Servo Control for Constrained Target Tracking of Fixed-Wing Unmanned Aerial Vehicles

This paper proposes an online predictive control method for fixed-wing unmanned aerial vehicles (UAVs) with a pan-tilt camera in target tracking. It aims to achieve long-term tracking while concurrently maintaining the target near the image center. Particularly, this work takes the UAV and pan-tilt camera as an overall system and deals with the target tracking problem via joint optimization, so that the tracking ability of the UAV can be improved. The image captured by the pan-tilt camera is the unique input associated with the target, and model predictive control (MPC) is used to solve the optimization problem with constraints that cannot be performed by the classic image-based visual servoing (IBVS). In addition to the dynamic constraint of the UAV, the perception constraint of the camera is also taken into consideration, which is described by the maximum distance between the target and the camera. The accurate detection of the target depends on the amount of its feature information contained in the image, which is highly related to the relative distance between the target and the camera. Moreover, considering the real-time requirements of practical applications, an MPC strategy based on soft constraints and a warm start is presented. Furthermore, a switching-based approach is proposed to return the target back to the perception range quickly once it exceeds the range, and the exponential asymptotic stability of the switched controller is proven as well. Both numerical and hardware-in-the-loop (HITL) simulations are conducted to verify the effectiveness and superiority of the proposed method compared with the existing method.

Robust Energy Efficiency Optimization Strategy for Emergency Communication Based on Fixed-Wing UAV

<p>The geographic location information of a user serves as the foundation for post-disaster emergency applications. However, uncertainties in the user positioning may arise due to factors such as building obstruction and damage to ground base stations. To ensure optimal communication quality, unmanned aerial vehicles (UAVs) can be implemented to maintain close proximity to the user with a minimal turning radius. However, a small turning radius can result in increased energy consumption due to propulsion requirements. To achieve an optimal balance between high communication throughput and low energy consumption, a robust energy efficiency optimization strategy is proposed based on a fixed-wing UAV, addressing the energy efficiency of emergency communication in imprecise user locations. First, the air-to-ground channel model is established, taking into account UAV propulsion energy consumption and formulating a multi-constraint problem to maximize emergency energy efficiency. Second, by incorporating the circular region method to address location uncertainty and considering the worst-case scenario, a robust problem is formulated. Finally, the worst-case scenario is addressed by utilizing the first-order Taylor approximation and successive convex approximation (SCA) technique to solve the nonconvex problem. Through simulation experiments, the proposed scheme is compared with three benchmark schemes, demonstrating its superior energy efficiency and robustness.</p> <p> </p>

Autonomous flight performance optimization of fixed-wing unmanned aerial vehicle with morphing wingtip

PurposeThe purpose of this paper is to improve autonomous flight performance of a fixed-wing unmanned aerial vehicle (UAV) via simultaneous morphing wingtip and control system design conducted with optimization, computational fluid dynamics (CFD) and machine learning approaches.Design/methodology/approachThe main wing of the UAV is redesigned with morphing wingtips capable of dihedral angle alteration by means of folding. Aircraft dynamic model is derived as equations depending only on wingtip dihedral angle via Nonlinear Least Squares regression machine learning algorithm. Data for the regression analyses are obtained by numerical (i.e. CFD) and analytical approaches. Simultaneous perturbation stochastic approximation (SPSA) is incorporated into the design process to determine the optimal wingtip dihedral angle and proportional-integral-derivative (PID) coefficients of the control system that maximizes autonomous flight performance. The performance is defined in terms of trajectory tracking quality parameters of rise time, settling time and overshoot. Obtained optimal design parameters are applied in flight simulations to test both longitudinal and lateral reference trajectory tracking.FindingsLongitudinal and lateral autonomous flight performances of the UAV are improved by redesigning the main wing with morphing wingtips and simultaneous estimation of PID coefficients and wingtip dihedral angle with SPSA optimization.Originality/valueThis paper originally discusses the simultaneous design of innovative morphing wingtip and UAV flight control system for autonomous flight performance improvement. The proposed simultaneous design idea is conducted with the SPSA optimization and a machine learning algorithm as a novel approach.

Low-cost development of a fully composite fixed-wing hybrid VTOL UAV

Fixed-wing hybrid vertical take-off and landing (VTOL) unmanned aerial vehicles (UAV) are popular due to their interoperability in the military and civilian domains, primarily where significant terrain difficulties exist for humans. Additionally, they can operate without requiring any runway infrastructure and have extended air endurance and efficiency. Since the hybrid UAV operates in distinct flight modes, viz., (a) VTOL and (b) fixed-wing cruise, carrying different payloads, the airframe structure requires careful design and manufacturing to realize sufficient strength. This experimental study aimed to identify the best combinations of various composite materials for manufacturing a lightweight, low-altitude long endurance (LALE) hybrid VTOL UAV. Primary materials include carbon fiber, Kevlar, fiber-reinforced plastic (FRP), resins, etc. Different rectangular test specimens of 120 × 5 mm size were made from ten different grades of carbon fiber, FRP, and resins using vacuum bagging. After properly curing these test specimens, we quantified their dynamic mechanical characteristics using various bending load experiments on a universal testing machine (UTM). An analysis of the experimental data facilitated the identification of the best composite combinations that provide maximum strength while reducing overall weight. Thus, we could understand the dynamic interplay between peak stress and test specimen weight. We also manufactured a UAV prototype using the identified combination and instrumented and flight-tested it to substantiate the experimental findings.

Optimization of high-speed fixed-wing UAV penetration strategy based on deep reinforcement learning

The penetration decision of unmanned aerial vehicles (UAVs) is one of the key components for UAVs to detect or strike crucial targets in adversarial environments. Presently, most methods lack research on penetration decisions under unknown interception information. The executing UAV for decision-making is generally a simplified three-degree-of-freedom model. The decision format is often simplified to path point optimization, lacking control over the stability of complex model attitudes and exhibiting relatively poor robustness. This paper proposes a penetration relative motion theory and aircraft control method based on the six-degrees-of-freedom UAV model to reduce response errors between algorithm control decisions and actual flight control. Using a Markov decision process, a UAV penetration decision control method based on an attitude-overload control loop is designed. This method combines reinforcement learning techniques to enable autonomous penetration decision-making and control for UAVs facing active defense scenarios, enhancing the autonomy, accuracy, and generalization of UAV online decision-making. The paper concludes by implementing autonomous decision-making and control for UAV penetration through the integration of interceptor trajectory prediction with proximal policy optimization. This method reduces the dependency of penetration strategies on interceptor information, ensuring both the generalization ability of penetration strategy models under uncertain information and the consistency of decision stability. Through simulations and experiments, we verified that the penetration strategy based on the pre-sampling PPO method improves the penetration effect by 15.44 % on average and the hit rate by 18.48 %. Moreover, the probability of penetration in different scenarios exceeds 70 %, and the penetration effect is improved by more than 10 % in the case of multiple interceptors. This paper also analyzes the impact of interceptor spatial distribution and the quantity of interceptors on the effectiveness of UAV penetration decision-making.

Designing a Morphing Wing for Fixed-Wing UAVs By IJISRT

This project report details the original conceptualization, design, and analysis of a morphing wing structure for fixed-wing Unmanned Aerial Vehicles (UAVs). Our approach incorporates an octahedron cell structure coupled with the application of shape memory alloys(SMAs) to facilitate controlled wing morphing. The primary goal is to augment the UAVs aerodynamic efficiency and adaptability to various flight conditions. The octahedron cell structure is employed as the foundational framework, providing a balance between structural integrity andflexibility for shape adjustments. The integration of shape memory alloys, known for their reversible phase transformations, enables precise and efficient control over the morphing process. Through this combination, we aim to optimize the UAVs flight characteristics, including improved efficiency, stability, and maneuver ability. To validate the feasibility and performance of the morphing wing design, Finite Element Analysis (FEA) has been conducted. This computational approach allows for a comprehensive evaluation of the structural integrity and aerodynamic behavior of the morphing wing under diverse loading conditions. The FEAresults offer crucial insights into the structural response, stress distribution, and deformation patterns, guiding the iterative refinement of the design for optimal functionality. This project represents a unique contribution to UAV technology, presenting an original perspective on morphing wing design that harnesses the benefits of octahedron cell structureand shape memory alloys. The insights gained from the FEA analysis provide valuable guidance for the ongoing development and implementation of morphing wing structures in fixed- wing UAVs. This work sets the stage for improved adaptability and performance in a range of operational scenarios, without reliance on external sources or existing research

MANUFAKTUR UAV FIXED WING MENGGUNAKAN MATERIAL STYROFOAM DENGAN METODE LAY-UP (PELAPISAN) FIBERGLASS DAN RESIN

This research discusses the development of a manufacturing process for a fixed wing-based UAV (Unmanned Aerial Vehicle) using Styrofoam material as the structural core. The manufacturing method applied is lay-up of fiberglass and resin to improve the strength and aerodynamic performance of the UAV. This research aims to evaluate the potential of styrofoam material enhanced by such lay-up technique. The research method involves the steps of applying fiberglass and resin lay-up on Styrofoam structure. The results of the Fixed Wing UAV Manufacturing research using Styrofoam material with fiberglass resin lay-up method, the aircraft structure becomes lighter and stronger with the overall resulting load reaching 2750 Gram or 2.75 kg. The results showed that the application of the lay-up method significantly improved the structural strength of the UAV, while the use of fiberglass and resin materials made a positive contribution to its aerodynamic performance. The invention has the potential to improve the efficiency and durability of UAVs in monitoring and surveillance applications. By presenting alternative materials and manufacturing methods, this research can contribute to the development of more reliable and effective UAV.

Derivation and Flight Test Validation of Maximum Rate of Climb during Takeoff for Fixed-Wing UAV Driven by Propeller Engine

In recent years, the use of fixed-wing Unmanned Aerial Vehicles (UAVs) has expanded, and the use of fixed-wing UAVs is expected to expand due to their usefulness for long-range operations. Different from manned aircraft, no provision is required regarding climb angle at takeoff for fixed-wing UAVs. Therefore, fixed-wing UAVs can take off by taking advantage of their performance. In addition, propeller engines are the propulsion device currently used by most fixed-wing UAVs. However, the thrust force generated by a propeller engine decreases as its airspeed increases. In such circumstances, this paper describes how to derive a maximum rate of climb in which the characteristics of the propeller engine are taken into account, with the aim of reducing takeoff time by maximizing the rate of climb during takeoff. The derivation uses optimization problems with a dependency of the thrust force on the airspeed. After the derivation of the maximum rate of climb, we first checked whether the maximum rate of climb obtained for the mass system was feasible for takeoff at the rate of climb by using a 6-DOF flight simulation, and then confirmed its validity through flight experiments.

On energy conservation for fixed-wing UAV assisted practical full-duplex relaying with service requirement and bank angle limit

This paper studies a full-duplex (FD) amplify-and-forward relaying, where a fixed-wing unmanned aerial vehicle (UAV) is dispatched as a mobile relay to serve a source-destination communication pair so as to satisfy their service requirement. Here, the UAV relay flies in a circular path and hence it must continuously change its heading of flight which leads to a bank angle of the aircraft. In order to ensure flight safety, therefore, a bank angle limit is imposed on the UAV relay. By considering a practical FD relaying, namely, there exists both residual loop interference (RLI) and processing time-delay at the relay side, novel signal to interference plus noise ratio (SINR) received at the destination is analyzed, giving a closed-form expression in addition to a concise lower-bound. Armed with the SINR analysis, the size of data received by the destination during a period of flight time of the UAV is calculated, leading to a closed-form lower-bound of the size of data. Taking bank angle limit into consideration and using the calculated lower-bound of the size of data, an optimization problem with the purpose of energy conservation is solved, leading to a novel method for joint adjustment of the UAV's flight parameters. Computer simulation experiments are conducted, and the results demonstrated that the proposed optimization method performs better in terms of energy conservation compared to the benchmark techniques regardless of the value of RLI and/or the service requirement of the source-destination communication pair.

L1 adaptive controller design for a tail fin controlled fixed wing unmanned aerial vehicle

The aim of this study is to design L1 adaptive controller, which is one of the robust control methods that can overcome model uncertainties, disturbances and noises, for a fixed-wing unmanned aerial vehicle (UAV) with tail fin controlled. In this context, first of all, the decoupled equations of motion of the six-degrees-of-freedom system are derived for the roll, pitch and yaw channels of the UAV. Then, the performance of the controller is demonstrated by simulation results for linearized system representation. By adding parameter errors to the system in question, the feature of tracking the given angle commands are analyzed. It has been observed that the L1 adaptive control structure exhibits rapid adaptation even in presence of system uncertainties. Finally, the controller is applied to the nonlinear system and operated throughout the entire flight envelope.