The droop-nose leading-edge morphing wing offers promising potential for reducing aerodynamic drag and noise during take-off and landing, thereby helping to lower aircraft fuel consumption and align with greener aviation goals outlined in Flightpath 2050 by the EU and ICAO declarations. Despite technological challenges and current technology readiness levels (TRL), droop-nose leading-edge (DNLE) wings are primarily tested and evaluated in unmanned aerial systems to reduce costs and risks. The literature proposes various optimisation methods for airfoil skin and morphing mechanisms; however, additional research contributions are needed to develop an effective design methodology. High actuator forces required for morphing, the trade-off between skin flexibility and load-bearing capacity, and the difficulty of obtaining smooth and continuous airfoil deformations are still under investigation. The present research introduces an optimisation methodology tailored for DNLE composite laminate skin and morphing mechanism structures. Its application to the UAS-S45 unmanned vehicle is utilised as a case study. Applying this design and optimisation methodology can lead to an 88% reduction in actuator mechanism force for a DNLE optimised for 6° angle-of-attack, considering an airfoil. This approach significantly enhances airfoil shape smoothness across sections and spanwise direction during morphing conditions. The proposed approach reduces the computational effort, as non-linear finite element method (FEM) analyses are not required within the optimisation loop, except at selected verification stages. A mechanism prototype was constructed to validate the FEM analyses and understand the limits of the simulation. Further investigations are required to achieve a morphing shape closer to aerodynamically optimised shapes.

This study aims to identify physiological features sensitive to human–automation trust (HAT) and to distinguish between high HAT (HHAT) and low HAT (LHAT) levels. A simulated aviation monitoring and takeover task was conducted under a 2 (reliability) × 2 (transparency) experimental design, in which HHAT and LHAT levels were defined based on their Jian scale scores. Paired t-tests were then employed to analyse HAT-sensitive features derived from five physiological modalities: electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), eye tracking, facial expression and electrocardiography (ECG). Finally, HAT discrimination models with varying interpretability were constructed using the identified sensitive features. The results indicated that: (1) compared with HHAT, LHAT exhibited significantly higher EEG $\alpha$ relative power and lower $\delta$ relative power, shorter eye-tracking metrics including the average duration of whole fixations and the average and total durations of fixations in takeover and flight areas, as well as higher ECG metrics such as the standard deviation of normal-to-normal intervals, the low-frequency to high-frequency ratio, and the low-frequency ratio; (2) no significant differences were found in fNIRS oxygenated haemoglobin and deoxygenated haemoglobin concentrations or in facial micro-expression and action units across HAT levels and (3) the highly interpretable linear support vector machine model constructed based on HAT-sensitive features achieved optimal performance, yielding a fivefold cross-validation accuracy of 0.8065 and an F1-score of 0.7942. These findings can provide empirical evidence for identifying HAT-sensitive physiological markers and developing real-time HAT assessment methods, thereby supporting the monitoring of trust degradation that may compromise flight safety.

This research is focused on the formulation, implementation and verification of novel transition control strategy for an originally designed tail-sitter VTOL hybrid UAV. A differential thrust transition vehicle was designed to achieve wide-envelope flight control by using four actuators. Attitude-tracking and altitude-hold controllers were developed to provide adequate control actuation for smooth transition between different flight modes. The control system was formulated for flight dynamic model of the aircraft, and implemented in MATLAB/Simulink environment for flight simulations. Tilt-angle-based feedback control was employed to calculate attitude errors. Altitude errors were obtained by direct comparison of commanded signal with the vehicle’s response. To maintain the desired altitude and attitude, required total thrust and differential thrust were calculated from the output of the altitude and attitude controllers, respectively. High-fidelity aerodynamic analysis was performed over the entire flight envelope to obtain the aerodynamic coefficients of the aircraft. Motor dynamics, propeller effects and airframe aerodynamics were incorporated in the flight dynamics model of the vehicle. Flight simulations verified effectiveness of the flight control system in tracking attitude and altitude-hold commands. The flight modes transition manoeuvre was successfully accomplished with the designed transition control system. The control technique for takeoff, hover and landing was successfully validated on manufactured prototype.

Demand forecasting is a key component of planning in the aeronautical sector, where intermittent and irregular demand patterns pose significant challenges for decision-making processes. Forecast combinations have emerged as an effective strategy to improve predictive performance by leveraging the complementary strengths of diverse models. However, the quality of such combinations depends heavily on the generation schemes for combination weights. Existing learning approaches, particularly those based on meta-learning, have shown strong performance but can require substantial computational resources, limiting their practicality in large-scale or real-time environments. This study investigates LightGBM, a learning algorithm based on decision trees, as an efficient meta-learning tool for generating forecast combination weights in intermittent demand, using real data from the aeronautical sector. The current study proposes a forecasting combination framework in which LightGBM learns to assign weights based on time series features, allowing for flexible adaptation to diverse demand patterns. The method is compared to XGBoost, particle swarm optimisation and traditional combination approaches across multiple forecasting horizons. The experiments include a diverse pool of standard forecasting models with parameter variations grounded in the literature, ensuring a wider range of behaviours and method diversity. Results show that the LightGBM combination achieves forecasting accuracy comparable to XGBoost (slightly better in some cases), while consistently reducing training times by $96.11{\rm{\% }}$ on average compared to state-of-the-art approaches. These findings demonstrate that the proposed LightGBM provides a scalable, high-performance solution for forecast combinations, particularly suited to environments with intermittent demand patterns.

The effect of the bio-inspired leading-edge modifications on the aerodynamic performance of non-slender delta wing models was investigated in a low-speed wind tunnel using force and surface pressure measurements. The measurements were performed at a Reynolds number of $Re = 1 \times {10^5}$ over an angle-of-attack range from $ - 4^\circ $ to $30^\circ $. Seven different sharp-edged delta wing models with a 45-degree sweep angle (${\rm{\varLambda }}$), including a base wing, were used to study the effect of sinusoidal and saw-tooth leading-edge modifications. Sinusoidal leading-edge wing designs were inspired by the leading-edge tubercles of the humpback whale’s pectoral fins. The results indicate that the bio-inspired wing modifications resulted in a delay in the stall angle by 4 degrees, smoother stall characteristics, a higher maximum lift coefficient, and increased post-stall lift. The drag coefficient of the modified wings was observed as higher than that of the base wing model. Regarding the longitudinal static stability, leading-edge modifications decreased the stability of the wing as the angle-of-attack surpassed $\alpha = 17^\circ $.

In the field of parafoil airdrop path planning, the inherent complexity and time-sensitive nature of mission requirements necessitate rapid path generation through low-order mathematical models that approximate the system’s true dynamics. This study presents a novel sparse identification framework for constructing a parafoil path planning approximate model. Leveraging high-fidelity 9-degree-of-freedom (9 DOF) dynamic simulation data as training inputs, our method identifies simple nonlinear relationships between 3D positional coordinates (for spatial targeting) and yaw angle (for directional control), which are critical path planning parameters. Compared to conventional 4 DOF models, experimental validation using field airdrop data reveals that the proposed sparse model achieves enhanced predictive accuracy while maintaining computational efficiency. Quantitative analysis demonstrates reductions in root mean square error (RMSE) by approximately 12.96% (horizontal position), 54.44% (height) and 37.96% (yaw angle). The efficacy is further confirmed through successful fixed-point homing across diverse initial deployment scenarios, underscoring its potential for parafoil path planning.

This study investigates the nonlinear dynamics and control strategies for a Cessna-182 unmanned aerial vehicle (UAV). The longitudinal and lateral dynamic models were derived using aerodynamic analysis conducted in ANSYS Fluent. Various control methodologies, including PID (proportional, integral, derivative) tuning with genetic algorithm (GA), root locus (RL), MATLAB tuned (MT), Ziegler Nichols (ZN), the model reference adaptive controller (MRAC), linear quadratic regulator (LQR) and linear quadratic Gaussian (LQG), were implemented and compared through simulations. Due to the inadequacy of PID control under variable environmental and noise conditions, LQR and LQG controllers, including the full state-space model required for real-time applications, are simulated for both longitudinal and lateral motions in addition to the PID controller. Although the LQR controller has acceptable simulation results for noiseless conditions, the superiority and stable structure of the LQG controller under noise and disturbance effects are highlighted. The results, including noise Dryden turbulence effects, highlight the advantages of proposed MRAC and LQG for robust stability and precise flight performance. Experimental flight tests validate the theoretical findings, demonstrating the practical viability of the proposed control approaches. The results obtained for the Cessna-182 mini-UAV will be effective for future researchers in terms of obtaining UAV dynamics and also evaluating different control strategies of the UAV.

In the process of utilising machine vision-assisted large aircraft component docking assembly, due to the occlusion induced by process equipment such as assembly tooling, the features on the calibration board cannot be extracted by each camera at the same time, resulting in calibration difficulties or calibration failure. This paper aims to propose a stereo calibration method for multi-cameras in large aircraft component assembly to improve calibration accuracy. Firstly, the sub-pixel edge extraction method based on Canny-Zernike is proposed to accurately extract the circular edges and circle centres of the calibration board, and the Zernike moment model is improved. The circle centre sorting method based on the triangular markers is introduced to realise the sorting of circle centres on the calibration board. Secondly, the intrinsic and extrinsic parameter models of multi-cameras and the visual parameter models between cameras are constructed, and Zhang’s calibration method and indirect calibration method are integrated to solve the parameters. Subsequently, the distortion correction model is optimised by Levenberg-Marquardt. Finally, experiments are performed to test the proposed method. The results show that the proposed method, compared with uncalibration and Zhang’s calibration method, the proposed method achieves stereo calibration of the multi-cameras under complex working conditions, enhances the calibration accuracy and improves the quality of the large aircraft component docking assembly.

X-wing unmanned aerial vehicles (UAVs) possess the unique capability to perform both bank-to-turn (BTT) and skid-to-turn (STT) manoeuvres by combining lift-vector tilting with sideslip-induced lateral force generation, thereby offering superior manoeuvrability compared with conventional fixed-wing platforms. In this study, a high-fidelity numerical investigation of X-wing manoeuvring performance is conducted using a six-degree-of-freedom (6-DOF) flight-dynamics model integrated with validated aerodynamic coefficients generated from USAF Digital DATCOM. The coupled effects of front-wing and rear X-tail anhedral–dihedral angles are systematically examined under both calm and crosswind conditions. The results indicate that increasing the combined angles significantly enhances lateral control authority and reduces the turning radius during STT manoeuvres, albeit at the expense of increased trajectory oscillations and heightened sensitivity to wind disturbances, whereas BTT manoeuvres exhibit comparatively smoother and more robust behaviour in crosswind environments. To further isolate the underlying physics, a dedicated roll- and yaw-control verification framework is developed to distinguish intrinsic aerodynamic characteristics from controller-induced effects, revealing distinct mechanisms for crosswind compensation. In addition, target interception simulations demonstrate that larger anhedral–dihedral angles improve interception performance, with the 90° configuration reducing interception time by approximately 16% and the minimum target separation distance by nearly 94%. Overall, this study elucidates the coupled aerodynamic–control mechanisms governing X-wing manoeuvrability and provides quantitative guidelines for the design of high-agility, wind-resilient UAV systems.

Collaborative robots (cobots) have emerged as a pivotal paradigm for the upcoming leap to Industry 5.0. In recent years, the range of applications has expanded significantly, particularly in assembly tasks within the manufacturing industry. The primary goal of this paper is to review the application of cobots in industrial assembly tasks, highlighting possibilities for innovative research in smart robotics, including prospects for challenging applications in aircraft final assembly processes. The paper systematically reviews recent literature to analyse the use of collaborative robotics in industrial assembly tasks, encompassing characterisation of application environments, motivations, characteristics and outcomes of relevant use cases across various industrial segments. Additionally, it reviews a set of innovative technological patents issued by the aeronautical industry over the past 14 years, highlighting trending projects in industry. The investigation reveals that the automotive and electronics industries remain at the forefront of cobot applications, mainly for tasks like pick-and-place operations and component manipulation. Applications in open work cells, where humans and robots operate at supportive or sequential interaction levels, using conventional communication interfaces and camera-assisted technologies, have been the most prevalent. The review identifies potential opportunities and key aspects from future application scenarios for cobots. The findings are relevant to the industrial robotics community, emphasising the need for novel applied research on human–robot colla boration in aeronautical industry.

Blended-wing-body (BWB) aircraft with distributed propulsion offers significant potential for aerodynamic efficiency. However, this tightly integrated configuration inherently operates under boundary layer ingestion (BLI) conditions. The ingested thick fuselage boundary layer, coupled with the strong aerodynamic interactions between the S-shaped intake and wing, generates adverse pressure gradients at the inlet alongside low total pressure recovery coefficients and high outlet distortion. This study investigates integrated flow control under high-altitude flight conditions using a BWB aerofoil integrated with a semi-embedded S-shaped intake model, incorporating energy analysis. During cruise, strong adverse pressure gradients between the wing shock wave and intake cause distinct lip separation, resulting in a low total pressure recovery coefficient and high distortion. Distributed dual-row suction generates secondary flow generates distributed secondary flow near the separation point with equivalent energy consumption. This enhances fuselage boundary layer mixing, reduces near-wall adverse pressure gradients and improves the total pressure recovery coefficient. The integration of internal jet control synergistically augments the benefits of external wing suction via distinct spatial mechanisms: central jets replenish core flow kinetic energy to suppress separation and improve the total pressure recovery coefficient, while side jets enhance mixing between separation zones and mainstream flow, mitigating secondary swirl losses and substantially reducing distortion at the aerodynamic interface plane (AIP). Energy analysis confirms that a modest energy input (equivalent to 7.4% of the mainstream kinetic energy) yields a significant increase in intake thrust, while simultaneously reducing total pressure and swirl distortion.

This paper addresses the challenge of obtaining accurate finite element model updating (FEMU) results for complex flexible aircraft structures without relying on prohibitively large numbers of direct finite element evaluations. An adaptive two-stage framework is proposed by integrating Bayesian regularisation neural networks (BR-ANN) with trust-region resampling. In the global stage, a coarse-grained surrogate model is constructed via Latin hypercube sampling (LHS) and Bayesian inference, enabling the non-dominated sorting genetic algorithm II (NSGA-II) to identify the Pareto-front region containing promising candidate solutions. Subsequently, a trust-region strategy performs high-fidelity resampling within the local neighbourhood of the knee-point solution, establishing a refined surrogate model that reduces local surrogate bias and improves parameter convergence. Validation using the GARTEUR SM-AG19 benchmark aircraft demonstrates that the proposed method effectively mitigates the smoothing effect inherent in conventional global surrogate models while maintaining computational efficiency. The average modal frequency error was reduced from 4.56% to 2.39%, and the method supports physically interpretable updating of material and equivalent beam parameters, providing a reliable baseline for subsequent aeroelastic predictions.

The privatisation of unmanned aerial vehicles (UAVS) used for situational awareness purposes to ensure their own situational awareness based on parameters gives direction to progress and provides a basic framework for future studies. In this context, a unique communication system architecture was proposed for obtaining state-of-the-art mini-UAV data and evaluations were carried out on the basis of data flow and parameters. Within the scope of the evaluation this study postulates a trailblazing approach as a means of optimising flight data pattern recognition by integrating Cross Industry Standard Process for Data Mining (CRISP-DM) and long short-term memory (LSTM)-based predictive depiction. By leveraging the structured framework of CRISP-DM and the sequential learning capabilities of LSTM, this research aims to enhance the accuracy of mini unmanned aerial vehicle systems (UAVS) flight scenario predictive reliability. This framework is applied to navigation phase, where the overall flight trajectory can be seen, and accurate forecasting and pattern recognition are critical for optimising operational efficiency. The findings have displayed the ability to perform high-accuracy predictions of flight parameters within a structured process. The experimental results demonstrate that key flight parameters can be predicted with near–comma-level numerical accuracy, indicating a high level of estimation precision. Through facilitating immediate data transfer and organised navigation phase evaluation, this research provides a methodical strategy for managing flight data, simultaneously contributing notably to UAV decision support systems and self-qualification engineering.

This study presents a comprehensive analysis of the frequency response characteristics in a gas generator cycle liquid rocket engine, employing modular decomposition and linearised frequency-domain modeling to simulate dynamic behaviours under forced oscillations. The engine is dissected into key subsystems, including liquid pipelines, turbopump assembly, valves, flow regulation components, thrust chamber, gas generator and pyrotechnic starter, highlighting features such as centrifugal pump pressurisation, staged combustion and cavitation mitigation via venturis. Three oscillation scenarios are examined: supply system responses to thrust chamber pressure disturbances, combustion component responses to fluid disturbances and combustion component responses to pump speed disturbances. Simulations over 0–2000 Hz reveal acoustic-dominated traits in the thrust chamber with oxidiser pathway dominance, low-frequency emphasis in the gas generator driven by fuel disturbances, and heightened instability risks from pump pulsations. Parametric analyses demonstrate that increased pipeline lengths shift resonant frequencies downward, elevated injector pressure drops enhance stability margins by 1.6% with a 20% pressure drop increase, and chamber structural/gas parameter variations erode system stability. These insights, validated against benchmark models, inform strategies for mitigating combustion instability, optimising design parameters, and improving reliability in high-thrust propulsion applications.

The present work investigates the thermochemical non-equilibrium effect in the DLR combustor using a two-temperature model combined with vibration-chemistry coupling model. Two operating conditions with inflow Mach 2 and 6 are selected for study. The simulation results illustrate that translational-vibrational non-equilibrium is related to energy transfer behaviour and the translational-vibrational relaxation time. When kinetic energy and chemical energy are converted into internal energy, there is a significant difference in the degree of conversion to translational and vibrational energy. If the translational-vibrational relaxation time is larger than the flow time, such as the relaxation time of the mainstream aftershock wave is 0.25 s for the condition with inflow Mach 2, and the flow time is 3 × 10−5 s, non-equilibrium will occur. Significant differences exist between the flow fields with Mach 2 and 6. A clear boundary layer separation occurs at Mach 6. Combustion occurs at the shear layer, which is in translational-vibrational equilibrium, and there are varying degrees of non-equilibrium in other locations. The dissociation of N2 and production of NO primarily occur on the strut walls and the upper/lower walls of the combustor. The mass fraction of NO is higher than the value at Mach 2. The combustion performance is influenced by the thermochemical non-equilibrium effect. At the condition of Mach 2, it increases the combustion efficiency by 10% near the injector and 0.27% at outlet relatively. Non-equilibrium inhibits the initial upstream combustion while slightly promoting downstream combustion under inflow Mach 6 condition.

In this article, a new triple-loop recurrent neural network and observer-based two-stage fast terminal sliding mode controller (TRNNO-TFTSMC) is proposed for a quadrotor unmanned aerial vehicle (UAV) with nonlinear dynamics and external disturbances. The two-stage fast terminal sliding mode control scheme is designed to guarantee fast convergence of trajectory tracking within a finite time. In the architecture of the flight control system, the triple-loop recurrent neural network (TRNN) is designed to approximate the nonlinear dynamics, which include system uncertainties and known nominal terms. Furthermore, to mitigate the impact of external disturbances and the approximation error of TRNN on the performance of the control system, an observer is employed. Finally, the closed-loop stability of the quadrotor system is ensured based on Lyapunov theory, and the outperformance of the proposed flight control scheme is clearly demonstrated through a comparative study with other techniques.

The demand for renewable energy is increasing rapidly due to the need for environmental sustainability and the reduction of energy costs. Solar energy, with its high potential and minimal environmental impact, has become a key energy source, particularly in innovative applications such as unmanned aerial vehicles (UAVs). The integration of solar cells into fixed-wing UAVs enables extended flight durations and enhances operational efficiency, making them a viable solution for long-endurance missions. In this study, the effects on flight performance, energy consumption and battery management of a fixed wing UAV equipped with solar cells are investigated. Two UAV models with the same aerodynamic characteristics, one with solar cells and the other powered only by the battery, are comparatively analysed. Using a simulation model, the effects of solar irradiation, temperature and environmental factors on the energy management of the UAV were evaluated. The findings indicate that the solar-powered UAV has the capability to sustain uninterrupted 24-hour flight by recharging its battery during daylight hours, whereas the battery-only model is restricted to approximately 14 hours of flight time. In the solar-powered model, dynamic altitude management strategies were employed to extend flight duration, utilising excess energy to determine optimal operational scenarios. Furthermore, the integration of solar cells was found to optimise battery consumption, thereby enabling more sustainable flight operations. These results demonstrate that solar-powered fixed-wing UAVs offer significant advantages for long duration missions and present an efficient solution for energy management.

As electric vertical takeoff and landing (eVTOL) aircraft technology advances, the focus has shifted towards hybrid electric power systems to overcome battery-specific energy limitations. This study introduces a deterministic hybrid power ratio to develop a series gas turbine hybrid electric propulsion system for a generic quad tiltrotor aircraft. First, the failure modes were categorised into two groups based on the primary power component arrangement, and the risks associated with each other were assessed. Three failure modes were identified in typical eVTOL layouts, i.e. one engine inoperative (OEI), one battery pack inoperative (OBI) and one proprotor inoperative (OPI). In addition, for configurations where a single nacelle contained both the battery and motor, a combined OPI+OBI case was considered, thereby acknowledging interconnected risks and extending the scope to four potential failure modes. The study determined the minimum weight of hybrid power systems using tailored deterministic hybrid power ratios based on five proposed sizing rules. In conclusion, the paper proposes an efficient battery layout for lightweight hybrid power systems and an optimal hybrid power system for the eVTOL aircraft, aligned with current battery technology levels.

This study investigates the effects of continuous atmospheric turbulence on the longitudinal and lateral-directional flight dynamics of a tailless unmanned combat aerial vehicle (UCAV) with a lambda wing planform. The absence of vertical tail surfaces reduces inherent stability and introduces strong coupling between flight modes, making disturbance rejection a critical challenge. The UCAV dynamics were linearised using small perturbation theory and represented in state-space form, with turbulence disturbances modelled using the Von Karman spectrum under light, moderate and severe intensities. The dynamic flight modes were evaluated with reference to the MIL-F-8785C flying quality requirements. State feedback and state observer-based controllers were initially designed using pole placement techniques to evaluate the performance of linear systems and gain insight into their flying qualities. A model predictive controller (MPC) was subsequently developed and compared with these controllers to assess their turbulence rejection and attitude tracking capabilities. Simulation results demonstrate that MPC provides superior robustness, effectively rejecting turbulence across all intensities while ensuring accurate pitch and roll attitude tracking. The findings provide new insights into the dynamics and control of tailless UCAVs under realistic turbulence conditions, supporting the viability of such configurations for mission-oriented applications.

The electrohydrodynamic force of a surface dielectric barrier discharge (SDBD) has been well-developed for flow control applications during recent decades. In the present paper, a geometrical modification of the SDBD plasma actuator has been applied to induce a vectorised normal flow at the trailing edge of a NACA0015 aerofoil. The pitot-tube velocity measurements of the normal jet along its propagation direction revealed formation of vortices at the centre of the electrode distance played a role in flow control authority of the jet. The aerodynamic operation of the double-SDBD structure as a virtual flap was assessed versus a single counter-flow jet of a floating structure at pre- and post-stall angles of attack at low Reynolds numbers. It was found that at small angles of attack, the steady counter-flow gives the most effectiveness of lift enhancement in low velocity, whereas in the higher velocity the unsteady one results in more efficacy. The efficiency of both steady and unsteady normal jets increased considerably at high angles such that a lift coefficient improvement of 38% was achieved at $\alpha = 14^\circ $. In the higher velocity, the plasma induced vertical flow acts like a Gurney flap, causing lift increase at high angles by affecting the vortical structures at the trailing edge. Evaluating the obtained results recommended employment of the induced normal flow as a virtual flap at high angles of attack in the unsteady actuation mode.