This paper presents an approach based on an intelligent fault-tolerant (FT) mechanism for enhancing a monopulse auto-tracking architecture through the integration of machine learning (ML) techniques. This approach is applied for the case of Oran ground station (GS) communicating in the S/X band frequency range to ensure system robustness during faulty events. The latter are due to the degradation or sudden failure of neuralgic radio frequency (RF) components, with an emphasis on the low-noise amplifier (LNA). To address these vulnerabilities, a probabilistic likelihood function is defined, and its statistical parameters are optimised using the grey wolf optimiser (GWO) in order to provide a means of automatic switchover to the ML tracking signal receiver (ML-TSR) control mode upon anomaly detection. Three ML algorithms, namely eXtreme Gradient Boosting (XGBoost), decision trees, and K-nearest neighbors (KNN), were implemented and evaluated using a database spanning eight years (2017–2024), including the operational parameters of the ALSAT-1B GS. The experimental results show that the KNN model achieves higher accuracy than the other approaches. A validation process was then conducted during an actual satellite pass encompassing a nominal phase followed by a GS LNA failure phase. During the nominal phase, a root mean square error (RMSE) of only 0.46° in azimuth and 0.64° in elevation was observed. Activation of our recovery mechanism during the second phase demonstrated that the robustness of the likelihood function, combined with the accuracy of the ML-TSR controller, ensures mission continuity without data loss, leading to four successful image downloads.

Aviation has a significant contribution to climate change, which is poised to increase in the coming years due to increasing demand in air travel. The A321 APPU aircraft could offer a significant improvement as it offers a synergistic combination of two interesting technologies – a fuel-flexible hydrogen combustion system combined with boundary layer ingestion, by introducing a hydrogen-powered auxiliary power and propulsion unit (APPU). This turboshaft engine is located in the tail cone and powers a boundary layer ingestion propulsor, producing approximately 15% of the thrust. To improve the efficiency of the APPU, the feasibility of the steam ijection and recovery (SIR) cycle is evaluated. This semi-closed water cycle can reduce fuel consumption and NOx emissions. Both the baseline and the SIR APPU are modelled in pyCycle, an open-source gas turbine parametric analysis tool. The baseline APPU engine was found to have a thermal efficiency of 45% and a mass of around 500 kg. The SIR cycle can reduce fuel consumption by up to 7% and decrease NOx emissions by approximately 33%, with an increase in engine mass of approximately 15%.

Area coverage optimisation is a hotspot in cooperative interception research for highly manoeuverable targets. In this paper, a geometric coverage-based fast approach is proposed to rapidly calculate cooperative interception regions for interceptors, achieving full coverage of the target predicted manoeuvering escape area. The method analytically derives both the required number of interceptors and the centre coordinates of each interceptable region. Simulation results verify the correctness and effectiveness of the proposed method. It demonstrates that for highly manoeuverable targets, this cooperative interception region calculation method can stably achieve full coverage of predicted manoeuvering escape areas while significantly reducing computation time, exhibiting excellent real-time performance.

Vertical take-off and landing (VTOL) vehicles are gaining traction in both the delivery drone market and passenger transportation, driving the development of urban air mobility (UAM) systems. UAM seeks to alleviate road congestion in dense urban areas by leveraging urban airspace. To handle UAM traffic, vertiport terminals (vertiminals) play a critical role in supporting VTOL vehicle operations such as take-offs, landings, taxiing, passenger boarding, refuelling or charging and maintenance. Efficient scheduling algorithms are essential to manage these operations and optimise vertiminal throughput while ensuring safety protocols. Unlike fixed-wing aircraft, which rely on runways for take-off and climbing in fixed directions, VTOL vehicles can utilise multiple surface directions for climbing and approach. This flexibility necessitates specialised scheduling methods. We propose a mixed integer linear programme (MILP) formulation to holistically optimise vertiminal operations, including taxiing, climbing (or approach) using multiple directions and turnaround at gates. The proposed MILP reduces delays by up to 50%. Additionally, we derive equations to compute upper bounds of the throughput capacity of vertiminals, considering its core elements: the touchdown and lift-off (TLOF) pad system, taxiway system and gate system. Our results demonstrate that the MILP achieves throughput levels consistent with the theoretical maximum derived from these equations. As a case study, we applied our throughput analysis on a vertiminal topology in the literature and used our MILP to find the optimal configuration. This dual approach, MILP and throughput analysis, allows for comprehensive capacity analysis without requiring simulations while enabling efficient scheduling through the MILP formulation.

As a revolutionary aviation propulsion system, the adaptive cycle engine (ACE) provides more powerful capabilities than conventional engines. To accurately exploit the ACE performance potential in a wide and complicated design space, the multi-objective optimisation towards enhancing the thrust and fuel efficiency based on the zero-dimensional and one-dimensional coupled (0D-1D) method is carried out. For the adaptive fan and variable geometry low-pressure turbine (LPT), the physics-based 1D mean-line models are established and coupled with the 0D engine model. Then, a multi-objective optimisation method is developed, which combines the back-propagation neural network (BPNN) and the non-dominated sorting genetic algorithm II (NSGA-II). At off-design conditions, the performance differences between the 0D-1D model and the 0D model are notable. In particular, when the 0D model is inaccurate in predicting the LPT characteristics with variable geometry, the ACE performance trends predicted by the 0D model may be opposite to reality. The Pareto-optimal thrust and fuel efficiency of the 0D-1D model are superior to those of the 0D model. Within the Pareto-optimal solutions, the subsonic specific fuel consumption (SFC) and thrust from the 0D-1D model can be 1.7% lower and 4.5% larger than those from the 0D model, respectively. The optimal design parameters and control laws between the two models are distinct.

In this study, a hybrid propulsion-powered small fixed-wing unmanned aerial vehicle (UAV) was designed to enhance endurance using solar energy. The UAV, a solar-powered vertical take-off and landing (VTOL) with a 1.8 m wingspan and a take-off mass of 3.3 kg, was equipped with a propulsion system comprising solar cells, a battery, a supercapacitor and a DC/DC converter, which was modelled in MATLAB/Simulink to evaluate energy management strategies. To optimise energy utilisation, fuzzy logic (FL), equivalent consumption minimisation strategy (ECMS) and quantum particle swarm optimisation (QPSO) algorithms were implemented. Notably, the QPSO algorithm was integrated into the solar energy management system for the first time. Optimisation results indicate that the QPSO algorithm harnesses solar energy more rapidly and efficiently than other strategies, significantly improving the UAV’s endurance. The time required for the QPSO algorithm to reach maximum power is 1.7948 s and 1.5028 s, shorter than that of the FL and ECMS algorithms, respectively. This result demonstrates that the QPSO algorithm exhibits a fast dynamic response and adapts more efficiently to sudden power demands. Furthermore, considering the time required to reach the maximum power output of 77 W from the solar cell, the corresponding contributions to endurance are calculated as 1.22 h for QPSO, 0.51 h for FL and 0.06 h for ECMS.

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.

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.

An active inceptor is a sidestick equipped with electromechanical actuators that provide programmable haptic feedback, offering pilots a tangible sense of control and enhancing their situational awareness. By integrating real-time force feedback mechanisms, active inceptors aim to improve handling qualities, reduce pilot workload, and support safer operations, particularly under dynamic or degraded flight conditions. Unlike conventional passive sidesticks, active inceptor systems (AIS) enable adaptive cueing strategies that respond to flight dynamics, control surface behaviour, and flight control laws. This review paper examines the evolving role of AIS in fly-by-wire (FBW) architectures and emerging aircraft control systems. It outlines fundamental design philosophies, summarises recent research and case studies, analyses its integration within flight control architectures, and reviews existing certification and regulatory considerations influencing AIS deployment. In addition, the paper explores potential handling quality assessment frameworks applicable to AIS. While the primary focus of this paper is the AIS application on fixed-wing aircraft, the review also highlights its established and emerging use in rotorcraft, offers insights into potential directions for future research and integration into next-generation flight platforms.

The widespread use of unmanned aerial vehicles (UAVs) has introduced significant security challenges, including unauthorised intrusions and privacy breaches. Conventional identification methods, such as radar and visual surveillance, often struggle to accurately distinguish between different UAV types or individual devices, limiting their effectiveness in countering these threats. To address these concerns, an enhanced empirical mode decomposition method has been developed. This method employs the Welch algorithm to extract power spectral density (PSD) features, which are then used to form the radio frequency fingerprint (RFF). Finally, a statistical classifier is employed to recognise and classify UAVs. The results of experimental studies indicate that this technique yields an average precision of 97% in binary classification (drone vs. no drone) and 84.7% in quaternary classifications (identifying four drone models). A comprehensive performance evaluation, conducted using confusion matrices and receiver operating characteristic (ROC) curves, demonstrates that the classifier performs well across all categories, with low misclassification rates.

This paper presents the design, modeling and control of a reconfigurable serial quadrotor (RSQ) with experimental validation. The proposed platform features servo-driven joints between its arms, enabling both ground and in-flight morphological reconfiguration. This capability allows the RSQ to adapt its geometry for navigation in confined environments and for aerial manipulation tasks without additional robotic arms. A comprehensive nonlinear dynamic model is developed to capture the effects of structural reconfiguration, explicitly accounting for time-varying inertia properties, their time derivatives, control allocation variations, and centre of gravity (CoG) shifts. These parameters are derived analytically and validated using high-fidelity computer-aided design (CAD) models across multiple configurations, enabling online model updating for simulation and control purposes. To address the strong dynamic coupling introduced by reconfiguration, a dedicated control architecture based on a proportional integral derivative (PID) controller is proposed. Closed-loop stability is analysed by casting the PID controller into a static output feedback form within a linear parameter varying (LPV) framework. The synthesis conditions are formulated as linear matrix inequalities (LMIs), and the resulting constrained optimisation problem is solved using the whale optimisation algorithm (WOA) to guarantee uniform exponential stability of the closed-loop system. The proposed approach is validated through nonlinear simulations and experimental tests on a custom-built test bench for multiple configurations. The results demonstrate accurate trajectory tracking and stable behaviour during structural changes, highlighting the potential of the RSQ for reconfigurable aerial robotics and manipulation applications.

Anomaly detection in piston engines plays a pivotal role in maintaining the continuous airworthiness and operational safety of general aviation aircraft. However, real-life data present several challenges, including diverse operating conditions, intermixed normal and abnormal samples and complex fault phenomena, all of which complicate model training and manual labeling tasks. To address the challenges, an improved deep autoencoder-based framework under multiple working conditions (IMDAEF) is proposed to enhance the accuracy of anomaly detection and the performance evaluation process. First, in the proposed method, a clustering-guided approach is introduced for working condition identification. Piston engine parameters are used for K-means clustering, and aircraft flight phase information is incorporated to fine-tune the results. Second, during the deep autoencoder training process, a sample selection mechanism and an equivalent threshold method are conducted, considering the presence of outliers in normal operation data. Finally, during the model evaluation stage, recognising the complexity of real-life fault phenomena that hinder manual labeling, an outlier region division and selection mechanism is proposed to re-annotate subtle fault occurrences in the fault datasets, thereby improving model evaluation accuracy. Results from four representative fault datasets confirm that the proposed framework maintains strong detection performance against complex faults. Moreover, the experiments demonstrate its ability to mitigate the negative impact of real-life data characteristics while meeting real-time performance requirements, offering valuable insights for future research.