Search Results (220)

In response to the issues of severe pitch oscillation and unstable roll attitude present in existing reinforcement learning-based aircraft cruise control methods during dynamic maneuvers, this paper proposes a precise control method for aircraft cruising based on proximal policy optimization (PPO) with nonlinear attitude constraints. This method first introduces a combination of long short-term memory (LSTM) and a fully connected layer (FC) to form the policy network of the PPO method, improving the algorithm’s learning efficiency for sequential data while avoiding feature compression. Secondly, it transforms cruise control into tracking target heading, altitude, and speed, achieving a mapping from motion states to optimal control actions within the policy network, and designs nonlinear constraints as the maximum reward intervals for pitch and roll to mitigate abnormal attitudes during maneuvers. Finally, a JSBSim simulation platform is established to train the network parameters, obtaining the optimal strategy for cruise control and achieving precise end-to-end control of the aircraft. Experimental results show that, compared to the cruise control method without dynamic constraints, the improved method reduces heading deviation by approximately 1.6° during ascent and 4.4° during descent, provides smoother pitch control, decreases steady-state altitude error by more than 1.5 m, and achieves higher accuracy in overlapping with the target trajectory during hexagonal trajectory tracking. Full article

This paper aims to analyze the performance of a tilt-rotor fixed-wing RPAS (Remotely Piloted Aircraft System) using a segmented approach, focusing on a nominal mission for SAR (Search and Rescue) applications. The study employs optimization techniques tailored to each segment to meet power consumption requirements, and the results highlight the accuracy of the physical characterization, which incorporates nonlinear propulsive and aerodynamic models derived from wind tunnel test campaigns. Critical segments for this nominal mission, such as the vertical take off or the transition from vertical to horizontal flight regimes, are addressed to fully understand the performance response of the aircraft. The proposed framework integrates experimental models into trajectory optimization procedures for each segment, enabling a realistic and modular analysis of energy use and aerodynamic performance. This approach provides valuable insights for both flight control design and future sizing iterations of convertible UAVs (Uncrewed Aerial Vehicles). Full article

Inconsistency in the structural strengths of a membrane wing under positive and negative loads has undesirable impacts on the aeroelastic deflections of the wing, which results in more significant flight control system modeling errors and worsens the performance of the aircraft. In this paper, an integrated dynamic model is derived for a membrane-wing aircraft based on the structural dynamics equation of the membrane wing and the flight dynamics equation of the traditional fixed wing. Based on state feedback control theory, an autopilot system is designed to unify the flight and control properties of different flight and wing deformation statuses. The system uses models of different operating regions to estimate the dynamic response of the vehicle and compares the estimation results with the sensor signals. Based on the compared results, the autopilot can identify the overall flight and select the correct operating region for the control system. By switching to the operating region with the minimum modeling error, the autopilot system maintains good flight performance while flying in turbulence. According to the simulation results, compared with traditional rigid aircraft autopilots, the proposed autopilot can reduce the absolute maximum attack angles by nearly 27% and the absolute maximum wingtip twist angles by nearly 25% under gust conditions. This enhanced robustness and stability performance demonstrates the autopilot’s significant potential for practical deployment in micro-aerial vehicles, particularly in applications demanding reliable operation under turbulent conditions, such as military surveillance, environmental monitoring, precision agriculture, or infrastructure inspection. Full article

Aircraft and fixed-wing drones, designed to perform vertical take-off and landing (VTOL), often incorporate unconventional configurations that offer unique capabilities but simultaneously pose significant challenges in flight mechanics modeling, whose reliability strongly depends on the correct tuning of the inertial and aerodynamic parameters. Having a good characterization of the aerodynamics represents a critical issue, especially in the design and optimization of unconventional aircraft configurations, when, indeed, one is bound to employ empirical or semi-empirical methods, devised for conventional geometries, that struggle to capture complex aerodynamic interactions. Alternatives such as high-fidelity computational fluid dynamics (CFD) simulations, although more accurate, are typically expensive and impractical for both preliminary design and lofting optimization. This work introduces a procedure that exploits multiple analyses conducted through semi-empirical methodologies implemented in the USAF Digital DATCOM to develop a flight mechanics model for fixed-wing unmanned aerial vehicles (UAVs). The reference UAV chosen to test the proposed procedure is the Dragonfly DS-1, an electric VTOL UAV developed by Overspace Aviation, featuring a three-surface configuration. The accuracy of the polar data, i.e., the lift and drag coefficients, is assessed through comparisons with computational fluid dynamics simulations and flight data. The main discrepancies are found in the drag estimation. The present work represents a preliminary investigation into the possible extension of semi-empirical methods, consolidated for traditional configurations, to unconventional aircraft so as to support early-stage UAV design. Full article

Unmanned tilt-rotor electric Vertical Take-Off and Landing (eVTOL) aircraft face significant control challenges during the transition from hover to forward flight, particularly when using open-source autopilot systems that rely on open-loop tilt control and static control gains. After the transition, the Total Energy Control System (TECS) becomes active in fixed-wing mode, but its default static gains often fail to correct energy imbalances, resulting in substantial altitude loss. This paper presents the Steepest Descent-based Total Energy Control System (SD-TECS), a real-time adaptive TECS framework that dynamically tunes gains using the steepest descent method to enhance post-transition altitude and airspeed regulation in unmanned tilt-rotor eVTOLs. The proposed method integrates gain adaptation directly into the TECS loop, optimizing control actions based on instantaneous flight states such as altitude and energy-rate errors. This enables improved responsiveness to nonlinear dynamics during the critical post-transition phase. Simulation results demonstrate that the SD-TECS approach significantly improves control performance compared to the default PX4 TECS, achieving a 35.5% reduction in the altitude settling time, a 57.3% improvement in the airspeed settling time, and a 66.1% decrease in the integrated altitude error. These improvements highlight the effectiveness of SD-TECS in enhancing the stability and reliability of unmanned tilt-rotor eVTOLs operating under autonomous control. Full article

The electric vertical take-off and landing fixed-wing (FW-VTOL) unmanned aerial vehicle (UAV) combines the advantages of fixed-wing aircraft and multi-rotor aircraft. Based on the cell discharge characteristics and the power system features, this paper proposes a preliminary design and optimization method suitable for electric FW-VTOL UAVs. The purpose of this method is to improve the design accuracy of electric propulsion systems and overall parameters when dealing with the special power and energy requirements of this type of aircraft. The core of this method involves testing the performance data of the cell inside the battery pack, using small-capacity cells as the basic unit for battery sizing, thereby constructing a power battery performance model. Additionally, it establishes optimization design models for propellers and rotors and develops a brushless DC motor performance model based on a first-order motor model and statistical data, ultimately achieving optimized matching of the propulsion system and completing the preliminary design of the entire aircraft. Using a battery discharge model established based on real cell parameters and test data, the impact of the discharge process on battery performance is evaluated at the cell level, reducing the subjectivity of battery performance evaluation compared to the constant power/energy density method used in traditional battery sizing processes. Furthermore, matching the optimization design of power and propulsion systems effectively improves the accuracy of the preliminary design for FW-VTOL UAVs. A design case of a 30 kg electric FW-VTOL UAV is conducted, along with the completion of flight tests. The design parameters obtained using the proposed method show minimal discrepancies with the actual data from the actual aircraft, confirming the effectiveness of the proposed method. Full article

The distributed electric tilt-rotor Unmanned Aerial Vehicle (UAV) combines the vertical take-off and landing (VTOL) capability of helicopters with the high-speed cruise performance of fixed-wing aircraft, offering a transformative solution for Urban Air Mobility (UAM). However, aerodynamic interference between rotors is a new challenge to improving their flight efficiency, especially the dynamic interactions during the transition phase of non-parallel tandem dual-rotor systems, which require in-depth investigation. This study focuses on the aerodynamic performance evolution of the tilt-rotor system during asynchronous transition processes, with an emphasis on quantifying the influence of rotor tilt angles. A customized experimental platform was developed to investigate a counter-rotating dual-rotor model with fixed axial separation. Key performance metrics, including thrust, torque, and power, were systematically measured at various tilt angles (0–90°) and rotational speeds (1500–3500 RPM). The aerodynamic coupling mechanisms between the front and rear rotor disks were analyzed. The experimental results indicate that the relative tilt angle of the dual rotors significantly affects aerodynamic interference between the rotors. In the forward tilt mode, the thrust of the aft rotor recovers when the tilt angle reaches 45°, while in the aft tilt mode, it requires a tilt angle of 75°. By optimizing the tilt configuration, the aerodynamic performance loss of the aft rotor due to rotor-to-rotor aerodynamic interference can be effectively mitigated. This study provides important insights for the aerodynamic performance optimization and transition control strategies of the distributed electric tilt-rotor UAV. Full article

This paper presents the development of a novel aerodynamic model tailored for the Bio-Inspired Rotating Empennage (BIRE), a non-traditional fixed-wing aircraft empennage inspired by avian flight. The BIRE replaces the conventional vertical stabilizer with an extra degree of freedom for the horizontal stabilizer, which is allowed to rotate about the body-fixed x axis. This empennage is similar to the tail of a bird, and allows control of both longitudinal and lateral moments. However, such a design introduces complex nonlinear longitudinal and lateral aerodynamic interactions, not typically accounted for in most fixed-wing aircraft aerodynamic models below stall. This work presents a nonlinear sinusoidal aerodynamic model that can be used for fixed-wing aircraft with this type of empennage. Although the aerodynamic model is constructed to accurately capture the degrees of freedom of this particular empennage design, similar methods could be used to develop other aerodynamic models for non-traditional control effectors. A large dataset of low-fidelity aerodynamic data was generated using a modern numerical lifting-line algorithm, and these data were fit to the nonlinear sinusoidal aerodynamic model. A method for fitting the data is demonstrated, and the results show that the nonlinear sinusoidal aerodynamic model can be fit to the data with an accuracy of less than 10% of the maximum deviation of the aerodynamic coefficients in root-mean-square error. The underlying physics of many of the longitudinal and lateral nonlinear sinusoidal aerodynamic properties of the aircraft are discussed in detail. The methodology presented here can be extended to other non-traditional control effectors, encouraging innovative approaches in aerodynamic modeling and aircraft design. In contrast, choosing to model control effectors using the traditional, linear approach can obscure key aerodynamic behaviors key for trim and control analyses. The study’s findings underscore the importance of developing adaptable aerodynamic models to support the advancement of next-generation aircraft designs and control systems. Full article

The aerodynamic performance of an aircraft can be enhanced by incorporating wingtip devices, or winglets, which primarily reduce lift-induced drag created by wingtip vortices. This study outlines an optimization procedure for implementing winglets on a Class I fixed-wing mini-UAV to maximize aerodynamic efficiency and performance. After the Conceptual and Preliminary design phases, a baseline UAV was developed without winglets, adhering to specific layout constraints (e.g., wingspan, length). Various winglet designs—plate and blended types with differing heights, cant angles, and sweep angles—were then created and assessed. A Computational Fluid Dynamics (CFD) analysis was conducted to evaluate the flow around both the winglet-free UAV and configurations with each winglet design. The simulations employed Reynolds-Averaged Navier-Stokes (RANS) equations coupled with the Spalart-Allmaras turbulence model, targeting the optimal winglet configuration for enhanced aerodynamic characteristics during cruise. Charts of lift, drag, pitching moment coefficients, and lift-to-drag ratios are presented, alongside flow contours illustrating vortex characteristics for both baseline and optimized configurations. Additionally, dynamic stability analyses examined how winglets impact the UAV’s stability and control. The results demonstrated a significant improvement in aerodynamic coefficients ($C_{Lmax}$, $L/D_{max}$, $C_{La}$, $C_{ma}$), leading to an increase in both range and endurance. Full article

This paper proposes a learning-based joint morphing and flight control framework for avian-inspired morphing aircraft. Firstly, a novel multi-objective multi-phase optimal control problem is formulated to synthesize the comprehensive flight missions, incorporating additional requirements such as fuel consumption, maneuverability, and agility of the morphing aircraft. Subsequently, an auxiliary problem, employing $ϵ$-constraint and augmented state methods, is introduced to yield a finite and locally Lipschitz continuous value function, which facilitates the construction of a neural network controller. Furthermore, a multi-phase pseudospectral method is derived to discretize the auxiliary problem and formulate the corresponding nonlinear programming problem, where open loop optimal solutions of the multi-task flight mission are generated. Finally, a learning-based feedback controller is established using data from the open loop solutions, where a temporal masked attention mechanism is developed to extract information from sequential data more efficiently. Simulation results demonstrate that the designed attention module in the learning scheme yields a significant 53.5% reduction in test loss compared to the baseline model. Additionally, the proposed learning-based joint morphing and flight controller achieves a 37.6% improvement in average tracking performance over the fixed wing configuration, while also satisfying performance requirements for fuel consumption, maneuverability, and agility. Full article

This paper presents a multi-fidelity optimization procedure for aircraft wing design, implemented in the early stages of the aircraft design process. Since wing shape is a key factor that influences aerodynamic performance, having an accurate estimate of its efficiency at the conceptual design phase is highly beneficial for aircraft designers. This study introduces a comprehensive optimization framework for designing the wing of a Class I fixed-wing mini-UAV with electric propulsion, focusing on maximizing aerodynamic efficiency and operational performance. Utilizing Class-Shape Transformation (CST) in combination with Surrogate-Based Optimization (SBO) techniques, the research first optimizes the airfoil shape to identify the most suitable airfoil for the UAV wing. Subsequently, SBO techniques are applied to generate wing geometries with varying characteristics, including aspect ratio ($AR$), taper ratio ($\lambda$), quarter-chord sweep angle (${\mathrm{\Lambda }}_{0.25}$), and tip twist angle ($\epsilon$). These geometries are then evaluated using both low- and high-fidelity aerodynamic simulations. The integration of SBO techniques enables an efficient exploration of the design space while minimizing the computational costs associated with iterative simulations. Specifically, the proposed SBO framework enhances the wing’s aerodynamic characteristics by optimizing the lift-to-drag ratio and reducing drag. Full article

In this article, the authors conducted electromagnetic radiation research on telecommunication base stations using Unmanned Aerial Vehicles (UAVs). Until now, UAVs have only been capable of performing visual inspections, without investigating electrical parameters. The authors suggest a method that involves using a spectrum analyzer and an automated flight with a predefined trajectory around the base stations to conduct electromagnetic radiation research, which is used by telecommunication regulatory organizations. Two different types of UAVs were used in this work: a drone and a fixed-wing aircraft, each with distinct characteristics. The authors successfully designed and tested both types of UAVs under real conditions and performed measurements. A specialized algorithm with software was developed for processing measurement results, which accurately presents the data in a graphical format. Experiments were performed, and the results, at distances of 200 m or further from the telecommunication base tower by changing the altitude by 5 m, were collected. Full article

Solar-powered unmanned aerial vehicles are fixed-wing aircraft designed to operate solely on solar power. Their defining feature is an advanced power system that uses solar cells to absorb sunlight during the day and convert it into electrical energy. Excess energy generated during flight can be stored in batteries, ensuring uninterrupted operation day and night. By harnessing the power of the sun, these aircraft offer key benefits such as extended flight endurance, reduced dependence on fossil fuels, and cost efficiency improvements. As a result, they have attracted considerable attention in a variety of military and civil applications, including surveillance, environmental monitoring, agriculture, communications, weather monitoring, and fire detection. This review presents selected aspects of the development and use of solar-powered aircraft. First, the general classification of unmanned aerial vehicles is presented. Then, the design process of solar-powered unmanned aerial vehicles is discussed, including issues such as the structure and materials used in solar-powered aircraft, the integration of solar cells into the wings, the selection of appropriate battery technologies, and the optimization of energy management to ensure their efficient and reliable operation. General information on the above areas is supplemented by the presentation of results discussed in the selected literature sources. Finally, the practical applications of solar-powered aircraft are discussed, with examples including surveillance, environmental monitoring, agriculture, and wildfire detection. The work is summarized via a discussion of the future research directions for the development of solar-powered aircraft. The review is intended to motivate further work focusing on the widespread use of clean, efficient, and environmentally friendly unmanned aerial vehicles for various applications. Full article

This paper describes flight planning supported by modeling, guidance, and feedback control for an electric Vertical Take-Off and Landing (eVTOL) QuadPlane small Uncrewed Aircraft System (sUAS). Five Lift+Cruise sUAS waypoint types are defined and used to construct smooth flight path geometries and acceleration profiles. Novel accelerated coverage flight plan segments for hover (Lift) and coverage (Cruise) waypoint types are defined as a complement to traditional fly-over, fly-by, and Dubins path waypoint transit solutions. Carrot-chasing guidance shows a tradeoff between tracking accuracy and control stability as a function of the carrot time step. Experimentally validated aerodynamic and thrust models for vertical, forward, and hybrid flight modes are developed as a function of airspeed and angle of attack from wind tunnel data. A QuadPlane feedback controller augments classical multicopter and fixed-wing controllers with a hybrid control mode that combines multicopter and aircraft control actuators to add a controllable pitch degree of freedom at the cost of increased energy use. Multi-mode flight simulations show Cruise mode to be the most energy efficient with a relatively large turning radius constraint, while quadrotor mode enables hover and smaller radius turns. Energy efficiency analysis over QuadPlane plans with modest inter-waypoint distances indicates cruise or aircraft mode is $30\%$ more energy efficient overall than quadrotor mode. Energy-aware coverage planner simulation results show fly-coverage (cruise) waypoints are always the most efficient given long distances between waypoints. A Pareto analysis of energy use versus area coverage is presented to analyze waypoint-type tradeoffs in case studies with closely spaced waypoints. Coverage planning and guidance methods from this paper can be applied to any Lift+Cruise aircraft configuration requiring waypoint flight mode optimization over energy and coverage metrics. Full article

This paper presents a methodology for training a Deep Learning model aimed at flight management tasks in a fixed-wing unmanned aerial vehicle (UAV), specifically autopilot control and GPS prediction. In this formulation, sensor data and the most recent GPS signal are first processed by an LSTM to produce an initial coordinate prediction. This preliminary estimate is then merged with additional sensor inputs and passed to an MLP, which replaces the conventional autopilot algorithm by generating the control commands for real-time navigation. The approach is particularly valuable in scenarios where the aircraft must follow a predetermined route—such as surveillance operations—or maintain a remote ground link under varying GPS availability. The study focuses on Class I UAVs; however, the proposed methodology can be adapted to larger classes (II and III) by adjusting sensor configurations and network parameters. To collect training data, a small fixed-wing aircraft was instrumented to record kinematic and control inputs, which then served as inputs to the neural network. Despite the limited sensor suite and the use of an open-source flight controller (SpeedyBee), the flexibility of the proposed approach allows for easy adaptation to more complex UAVs equipped with additional sensors, potentially improving prediction accuracy. The performance of the neural network, implemented in PyTorch, was evaluated by comparing its predicted data with actual flight logs. In addition, the method has been shown to be robust to both short and prolonged GPS outages, as it relies on waypoint-based navigation along previously explored routes, ensuring reliable performance in known operational contexts. Full article