This paper presents a detailed robustness analysis of three nonlinear filtering algorithms: the unscented Kalman filter, the cubature Kalman filter, and the ensemble Kalman filter, applied to aircraft state estimation for fixed-wing flight dynamics. The study focuses on estimating critical longitudinal flight parameters such as true airspeed, angle-of-attack, pitch angle and pitch rate using pitch angle measurements. A nonlinear aircraft model is formulated, and each filtering technique is implemented and evaluated under multiple scenarios, including sensor noise, initial state mismatches and plant-model uncertainties. Simulation results across four cases, ranging from ideal conditions to $95{\mathrm{\% }}$ mismatch, demonstrate that the unscented Kalman filter consistently delivers the most accurate and robust estimates, especially for velocity and pitch rate. The cubature Kalman filter offers a trade-off between estimation accuracy and computational efficiency, while the ensemble Kalman filter shows significant sensitivity to uncertainties but performs relatively better in estimating the angle-of-attack under severe mismatch conditions. This comparative study provides valuable insights for selecting appropriate filtering strategies in aerospace applications, particularly where robustness and reliability under uncertainty are crucial.

Systems are holistic that represent complex structures in which different components interact. Systems thinking plays an important role as a methodology used to solve and analyse these complex problems. This approach emphasises addressing problems holistically rather than simply breaking them down into parts, providing a deeper understanding of identifying and resolving root causes. The article aims to explain the conceptual framework of systems thinking by discussing the basic concepts and principles of systems thinking in detail. In this context, the literature focuses on reaching a common definition of the term ‘systems’ and discusses practical approaches to the use of systems thinking in aircraft design. It also includes analysis of the application of systems thinking through examples of catastrophic accidents resulting from the misunderstanding or mismanagement of complex systems in engineering studies. The change in aircraft design process over the years has been examined, and a new categorisation method is proposed. By integrating systems thinking into the aircraft design process, it examines the advantages it will provide in understanding and optimising the interaction of components, saving time and costs. This study aims to deal with the systems thinking perspective of aircraft design. The importance of the system concept in aviation is emphasised with concrete examples, and its applicability is examined. Thus, a base is formed for its use in aviation.

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.

This study proposes a radial basis function neural network disturbance observer- (RBFNNDO) based anti-saturation backstepping controller for hypersonic vehicles with input saturations and multiple disturbances. Firstly, in response to the problem of ‘exploding complexity’ in backstepping controller, we adopt finite-time tracking differentiators (FTD), which realise higher tracking accuracy and tracking speed than those of the existing methods. Secondly, we develop multivariable neural network disturbance observers to estimate the lumped disturbances involving aerodynamic uncertainties and external disturbances, thereby improving the robustness of the proposed controller. Thirdly, in order to alleviate the input saturation and minimise the duration time, we use an adaptive fixed-time anti-saturation compensator (AFAC). The simulation results have proven that our proposed backstepping controller outperforms other existing methods in terms of control performance and saturation time.

To enhance the aerodynamic performance of hypersonic nozzles, a three-dimensional method of characteristics (3D MOC) is proposed for designing single expansion ramp nozzles (SERN). The flowfield structures and aerodynamic performances of nozzles designed using the 3D and conventional two-dimensional (2D) MOC approaches are compared through three-dimensional numerical simulations. Results indicate that the major geometric differences occur along the circumferential wall. Under typical flight conditions, the nozzle designed by 3D MOC achieves over 0.45% higher axial thrust coefficient and more than 8% higher lift coefficient than the 2D MOC design. Furthermore, based on the 3D MOC, both rectangular nozzle (RN) and circular nozzle (CN) configurations are designed and analysed. The CN exhibits slightly superior aerodynamic performance compared with the RN. By further modifying the upper and lower wall infill surfaces of the RN, three new nozzle variants are obtained. Comparative results show that these geometric infills have a pronounced influence on lift and pitching moment, while the axial thrust coefficient remains nearly constant. Finally, the effect of the tracing radius on the RN and CN configurations is examined. Increasing the tracing radius notably reduces the lift coefficient but has minimal impact on the thrust and pitching moment. These findings highlight the potential of the 3D MOC-based design method to flexibly balance aerodynamic moment and lift in hypersonic nozzle optimisation.

Birds and bats are extremely adept flyers: whether in hunting prey, or evading predators, post-stall manoeuvrability is a characteristic of vital importance. Their performance, in this regard, greatly exceeds that of uncrewed aerial vehicles (UAVs) of similar scale. Attempts to attain post-stall manoeuvrability, or supermanoeuvrability, in UAVs have typically focused on thrust-vectoring technology. Here we show that biomimetic wing morphing offers an additional pathway to classical supermanoeuvrability, as well as novel forms of bioinspired post-stall manoeuvrability. Using a state-of-the-art flight simulator, equipped with a multibody model of lifting surface motion and a delay differential equation (Goman-Khrabrov) dynamic stall model for all lifting surfaces, we demonstrate the capability of a biomimetic morphing-wing UAV for two post-stall manoeuvres: a classical rapid nose-pointing-and-shooting (RaNPAS) manoeuvre; and a wall landing manoeuvre inspired by biological ballistic transitions. We show that parametric variation of nonlinear longitudinal stability profiles is an effective open-loop strategy to explore the space of post-stall manoeuvres in these types of UAVs; and it yields insight into effective morphing kinematics to enable these manoeuvres. Our results demonstrate the capability of morphing-based control of nonlinear longitudinal stability to enable complex forms of transient supermanoeuvrability in UAVs.

This study presents a hybrid energy management system (EMS) for unmanned aerial vehicles (UAVs), integrating flexible photovoltaic (PV) panels with a high-capacity lithium-polymer (Li-Po) battery to enhance flight endurance and energy resilience. The proposed EMS features a multi-layered adaptive control architecture, combining fuzzy logic-based decision-making, short-term solar irradiance prediction using machine learning and closed-loop proportional-integral-derivative (PID) motor control. This architecture dynamically allocates power based on real-time solar irradiance, battery state of charge (SOC) and propulsion load. The approach aims to minimise battery stress while ensuring stable UAV operation under variable environmental conditions. Simulation results indicate that, without PV support, the UAV battery depletes in approximately 28 min. However, incorporating an 80 W PV panel extends the endurance to 37 min, and a 160 W effective PV configuration increases the flight time to over 50 min, while maintaining the SOC above 20%. This corresponds to a 79% improvement in endurance and nearly a 50% reduction in net battery energy consumption. The EMS effectively mitigates deep discharge cycles, improving battery health and operational lifespan. These findings demonstrate that the proposed hybrid EMS provides a scalable, adaptive and computationally efficient solution for balancing renewable and stored energy. It outperforms conventional static or single-source control strategies, particularly under fluctuating solar conditions.

The air data system (ADS) consists of multiple sensors designed to measure atmospheric parameters during an aircraft’s flight. These measurements are then processed by the air data computer to compute the relevant flight parameters required for guidance and control. However, due to the system’s inherent complexity and the harsh flight environment, internal sensors are highly susceptible to failures, including scenarios where multiple components fail simultaneously. To address the issue of complex fault diagnosis in the aircraft air data system, this paper investigates a fault diagnosis method based on the unscented Kalman filter (UKF) and capsule network (CapsNet). First, a UKF model for atmospheric parameters is established based on the computational principles of the air data system, and parameters that can effectively reflect fault information are selected. Then, feature mode decomposition is applied to enhance fault characteristics, which are used as inputs for the neural network. Next, the convolutional layers of the CapsNet are improved using the InceptionV3 network to enhance feature extraction capabilities, and a multi-label classifier is designed to achieve fault location of compound faults. Finally, the proposed algorithm is validated on the simulation platform. Through ablation and comparative experiments, the proposed algorithm achieves a fault diagnosis accuracy of over 95%, demonstrating a more effective solution to the compound fault problem in the aircraft air data system. In our study, compared to convolutional neural networks (CNN), fully convolutional network (FCN), temporal convolutional network (TCN), Inception V3 and the original CapsNet, the improved CapsNet achieved the best performance in compound fault diagnosis.

It is well known that the amount of damage caused by lightning strikes to protected composite airframe structures depends on the paint characteristics, often applied on the surface of composite structures to protect from environmental effects and to personalise a product. In this work, physically based models of the mechanical loads induced by lightning strikes are employed in the generation of the mechanical overpressure fields due to a simulated lightning strike, while accounting for the paint thickness. These fields are then implemented into a three-dimensional finite element framework and combined with a damage model to predict the effect of paint thickness on the mechanical damage in composite structures subjected to this type of events. These models accurately predict the increase of damage extent with the increase of paint thickness, which is corroborated by experimental observations from industry and by the experimentally observed trends reported in literature.

This study proposes an efficient aerodynamic prediction and optimisation framework integrating blade element momentum theory (BEMT), a Bayesian-optimised aerodynamic residual network (BO-ARN), and a genetic algorithm (GA) for high-altitude long-endurance solar UAV propellers under stratospheric low-Reynolds-number conditions. An aerodynamic database was generated using Latin hypercube sampling and XFoil. A fully connected residual network was developed to learn nonlinear mappings from Reynolds number, Mach number and angle-of-attack to lift and drag coefficients, while tree-structured Parzen estimator Bayesian optimisation selected network depth, width, learning rate, weight decay and batch size. The trained BO-ARN surrogate was embedded in BEMT to replace repeated XFoil evaluations, and a GA was then used to optimise the spanwise chord and twist distributions under motor power and torque constraints. Compared with a Bayesian-optimised multilayer perceptron, BO-ARN reduced the RMSEs of lift and drag coefficient prediction by 25.65% and 37.80% on the training set and by 4.06% and 7.71% on the test set, respectively. For one propeller operating condition, BO-ARN-supported BEMT reduced the calculation time from 43.322 s to 0.411 s. After optimisation, cruise thrust increased from 17.89 N to 19.49 N, corresponding to an 8.94% improvement, while system propulsion efficiency increased from 68.75% to 70.59%, with the motor constraints satisfied. These results demonstrate that the proposed framework provides an accurate and computationally efficient approach for propeller-motor matching and aerodynamic shape optimisation of high-altitude UAV propulsion systems.

Standard quadrotors exhibit limited mobility due to inherent underactuation: they only have four independent control inputs, whereas their position and attitude in space are defined by six degrees of freedom (DOF). Consequently, a quadrotor’s pose cannot track an arbitrary trajectory over time. To address this limitation, a novel actuation concept has been proposed, wherein the quadrotor’s propellers can tilt around their axes relative to the main body–forming a vector quadrotor. To achieve more accurate trajectory tracking tailored to the specific characteristics of this vector quadrotor model, we propose a novel control strategy. First, we integrate special orthogonal group SO(3) theory with model compensation control: SO(3) theory enables accurate modeling of the aircraft’s rotational dynamics, while model compensation control mitigates unmodeled dynamics and external disturbances, thereby ensuring robustness across diverse operating conditions. Second, we introduce the sequential quadratic programming (SQP) method for control allocation; this method not only enables efficient computation of control inputs but also optimises the allocation of control resources, which enhances system performance–particularly in complex manoeuvering scenarios. Finally, we integrate the SO(3)-based controller with the SQP-based control allocation module to form a unified control system. The effectiveness of this proposed approach is validated via simulation results. These results demonstrate improved trajectory tracking accuracy and enhanced robustness against disturbances, thus confirming the potential of our method for practical applications.

Gas turbine maintenance strategy relies heavily on accurate estimation of critical component life consumption of gas turbine engines during their operations. The equivalent operating hours (EOH) is a useful concept to measure the engine life consumption and support condition-based maintenance planning for gas turbine engines and their critical components. However, the current EOH calculation methods are mostly empirical and engine-specific, relying on vast operating data and experience. This paper introduces a novel physics-based method to estimate the EOH of the high-pressure turbine rotor blades of a gas turbine engine based on the damages caused by creep and low-cycle fatigue (creep-LCF) interactions. The method has been applied to a typical turbofan engine taking both 440-minute long-haul flight at one flight per day and 60-minute short-haul flight at two flights per day. A comparison of the predicted damages and life consumptions indicates that the creep EOH and also the creep damage of the engine of the short-haul aircraft is about 1.38 times that of the engine of the long-haul aircraft, the LCF equivalent operating cycles (EOC) and also the LCF damage of the engine of the short-haul aircraft is about 2.0 times that of the engine of the long-haul aircraft, and the total damages are more affected by the creep damage than the LCF damage with the creep damage being 6.78 times the LCF damage for the engine of the short-haul aircraft and 9.81 times for the engine of the long-haul aircraft. In addition, the total EOH or the total damage of the engine of the short-haul aircraft is about 1.44 times that of the engine of the long-haul aircraft. The proposed method shows a great potential to provide a quick estimate of the life consumption of gas turbine engines for condition monitoring, and it can be applied to other types of gas turbine engines.

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.

The nonlinear behaviour and parameter coupling inherent in gas turbine engines pose persistent challenges in the development of accurate thrust prediction models for unmanned aerial vehicle (UAV) propulsion systems. This study investigates the application of ensemble regression methods to address these complexities and enhance predictive accuracy using experimental data obtained under controlled test conditions. The evaluated models include bootstrap aggregating (bagging), adaptive boosting (AdaBoost), light gradient boosting machine (LightGBM), CatBoost, stacked generalisation (stacking) and a voting regressor ensemble. Each model was trained and evaluated using experimentally measured thermodynamic and operational parameters across a range of engine operating conditions. Cross-validation results revealed that stacking achieved the lowest prediction errors with a root mean square error (RMSE) of 0.0635 and a mean absolute error (MAE) of 0.0442, while AdaBoost demonstrated a very high cross-validated coefficient of determination (${R^2}$) of 0.9998. CatBoost achieved ${R^2}$ values above 0.99, and voting produced ${R^2}$ values close to 0.99. LightGBM exhibited comparatively higher prediction errors and greater variability across folds. These results indicate the potential effectiveness of stacking and adaptive boosting approaches for thrust estimation in UAV gas turbine propulsion systems, while also demonstrating that ensemble performance depends strongly on algorithm selection and dataset characteristics.

Aircraft arresting systems (AAS) are critical safety components required for the operation of fighter aircraft, particularly during aborted take-off or emergency landing scenarios. The most significant challenge in the development and certification of these systems is the high-cost full-scale dead-load testing required to verify their performance under various aircraft mass and entry-speed conditions. This study presents a novel and fully domestic test system that reproduces the kinetic energy input defined in the MIL-STD-3036 standard by using high-inertia flywheels driven by electric servomotors. The proposed system stores the required energy in dual flywheels and transfers it to the arresting barrier through a controlled release mechanism, thereby replicating real aircraft-entry conditions without the need for jet engines, runways or physical dead-load vehicles. The study focuses on the conceptual design and analytical modelling of the proposed system. Dynamic analyses were conducted to determine flywheel geometry, material selection, allowable stresses, rotational speeds and energy absorption capacity. The system was shown to meet the highest energy level defined in the MIL-STD-3036 test matrix, while being designed to enable accurate and controlled evaluation of arresting force, torque response, angular deceleration and stopping time. Experimental validation has not yet been conducted and is planned as part of future work. The results demonstrate that the proposed test system provides an economical, repeatable and safe alternative to conventional dead-load tests and enables performance evaluation, maintenance verification and certification of AAS. Furthermore, the system offers a scalable platform for future arresting technologies, including those designed for unmanned aircraft and next-generation military platforms.

A novel fixed-time cooperative guidance law is developed to enable multiple flight vehicles to simultaneously intercept various target motions with desired impact angle, including stationary, constant-velocity moving and manoeuvring targets, with a unified guidance structure that requires no mode switching or target motion classification. First, a fixed-time distributed cooperative guidance law is developed using the theory of multi-intelligence cooperative control, which is formulated along the line of sight (LOS) direction. This approach ensures that the impact time is regulated, enabling multiple flight vehicles to intercept the target simultaneously within a fixed time. In the second phase, employing a sliding manifold and a fixed-time reaching law, the normal acceleration of each flight vehicle is designed to ensure that the LOS angle converges to its target value within a fixed duration. Unknown components of the target’s acceleration are estimated via fixed-time observers and incorporated into the guidance commands, enhancing precision and robustness in the guidance process. In conclusion, the proposed approach proves the effectiveness and superiority through numerical simulations, including various target motions, switching communication topology, robustness against uncertainties based on Monte Carlo experiment and comparative studies.

This study presents a bespoke hardware platform for indoor navigation, featuring a quadrotor equipped with an FZ3 card incorporating the AMD (formerly Xilinx) Zynq UltraScale+ ZU3EG MPSoC as the onboard computer. A core component of this platform is a field programmable gate array (FPGA) module specifically designed to efficiently compute Delaunay triangulations, enabling enhanced spatial awareness and real-time surface reconstruction. The onboard computer communicates with the flight controller, inertial measurement unit (IMU), ultra-wideband (UWB) localisation system, stereo camera, light detection and ranging (LiDAR) and ultrasonic sensors via robotic operating system (ROS) 2. The primary objective is to develop a cost-effective, modular unmanned aerial vehicle (UAV) system that can be adapted for a range of indoor navigation applications. The modular design supports different onboard computer platforms and sensor configurations, allowing researchers to easily customise the system for various experiments. By providing a practical framework for precise indoor navigation, this platform addresses the limitations of simulated, simplified laboratory setups, accelerating prototyping and supporting the deployment of UAVs in complex real-world environments. This work explores the UAV’s hardware architecture, the implementation of the Delaunay triangulation core on the FPGA system-on-chip (SoC), the ROS 2-based communication system and includes a detailed mass analysis and power estimation.

Flow separation in highly loaded axial compressors remains a major barrier to performance, motivating the search for active flow control strategies. This study investigates air injection to energise low-momentum endwall flow in a tandem stator configuration, representing the first investigation of its kind for tandem vanes. A numerical investigation was conducted, starting with a smooth-casing reference case and progressing to parametric studies of slot geometry (inclination $\alpha $, jet angle $\beta $, radius of curvature ${R_c}$, circumferential width ${w_c}$), relative injection mass flow rate ${\dot m_{inj}}/{\dot m_{stall}}$ and axial location $\zeta $. The results show how each parameter influences efficiency and pressure ratio, yielding design guidelines: shallow $\alpha $, moderate $\beta $ towards the separation zone, relatively large ${R_c}$ and a balanced ${w_c}$–${\dot m_{inj}}/{\dot m_{stall}}$ combination, best captured through the momentum coefficient ${C_u}$ and velocity ratio ${u_{inj}}/{u_\infty }$. Injection near $\zeta \approx 1.2$ (just upstream of separation) proved most effective, and off-design simulations showed larger efficiency gains towards de-throttled conditions, although stall margin was unaffected. Robustness was confirmed through turbulence-model comparisons and injector turbulence variations, which consistently reproduced suppression of suction-side separation. An integrated analysis of aerodynamic losses further showed that injection strategies remain beneficial when loss penalties are considered. The study thus establishes transferable guidelines for injector design in tandem stators, providing a foundation for future optimisation and experimental validation.

Unmanned aerial vehicles (UAVs) tracking one moving target in an oilfield environment with dynamic obstacles has been studied. Firstly, the UAV tracking one target is modeled mathematically, including the oilfield environment, the UAV kinematic model, the moving target model and so on. Considering the oilfield environment with dynamic obstacles, the chance-constraint method is incorporated into the distributed model predictive control (DMPC) to realise the target tracking and avoid the obstacles in the environment. Secondly, to obtain high-quality control-input sequences, the improved salp swarm algorithm (ISSA) is designed as the solver for the chance-constraint DMPC, integrating the sine-tent-cosine chaotic mapping in the initialisation, the differential mutation operation in the leader-position updating and the nonlinear adaptive inertia weights in the follower-position updating. Finally, the simulation results show that the proposed method has great performance in tracking the moving target in an oilfield environment with dynamic obstacles.

This paper presents a comprehensive experimental investigation into the shock characteristics associated with a low-thrust, low-shock separation mechanism incorporating Mild Detonating Cord (MDC) within a rubber bellow interface. Two test configurations were developed with varying explosive charge masses to study their influence on pressure generation and shock propagation. Linear accelerometers and high-speed pressure transducers were employed to capture transient dynamic responses at both piston and cylinder interfaces. The results demonstrate a significant reduction in peak pressure and shock levels, especially in the second test configuration, where the explosive mass was reduced to 60% of the initial configuration. The shock response spectrum (SRS) analysis confirms that the lower charge mass leads to proportionally reduced shock amplitudes across the frequency range of interest. Furthermore, comparative assessment of shock levels reveals a significant reduction of shock levels as compared to conventional separation mechanisms, such as a flexible linear-shaped explosive charge (FLSC) mass or a separation bolt actuated with a pyro cartridge. The experimental pressure values are shown to correlate well with theoretical predictions, validating the design approach. These findings provide critical insights into tailoring explosive-based separation mechanisms for sensitive payload environments, highlighting the importance of confined detonation and charge optimisation in mitigating pyroshock.