The paper addresses the six-degree-of-freedom coupled control problem for spacecraft formation flying subject to actuator saturation and input quantisation whilst considering limited communication resources. Firstly, a novel event-triggered distributed observer without continuous communications is presented to recover the information of the virtual leader. Remarkably, by embedding a hyperbolic tangent function-based nonlinear term into the triggering condition, the event-based observer realises a more reasonable trigger threshold. Subsequently, an adding-a-power-integrator-based fixed-time control algorithm is proposed for the follower spacecraft. Further, the control scheme ingeniously compensates for the actuator saturation and the input quantisation problems without embedding auxiliary systems. Finally, numerical simulations are carried out to highlight the advantages of the theoretical results.

Friction stir welding is a prominent technique for making defect-free joints of aluminum alloys. The aluminum alloy AA2014-T6 finds wide applications in aerospace, naval and automotive applications. This paper attempts to predict the tensile strength and hardness characteristics of friction stir welded aluminum alloy AA2014-T6 by a fuzzy logic model. Friction stir welding was carried out by varying tool rotational speed (700, 1,000 and 1,400rpm), welding speed (20, 35, 50mm/min) and axial force (10, 12, 14kN) at three levels. The tensile strength and hardness characteristics of the welded specimens were obtained from the experiments conducted as per Taguchi’s L27 orthogonal array. A Mamdani-type fuzzy logic model was developed to predict the tensile strength and nugget hardness characteristics of the FSW joints. The fuzzy model was evaluated by comparing the results of confirmation experiments with that of the results predicted by the model. The confirmation experiments were conducted with a new set of parameters other than the ones used for building the model. The fuzzy model exhibits marginal variations of 2.53% for tensile strength and 2.42% for weld nugget hardness compared to the results of the conformation experiments.

In the preliminary design phase of aircraft design, estimating the production cost accurately is a challenging task. At this stage, many design parameters that affect the overall cost are still undefined. This paper establishes cost-estimation models for civil, commercial aircraft using a parametric cost analysis (PCA) approach. Aircraft are characterised based on their size, ranging from a wide body to executive jets, into four categories. Key design parameters, such as maximum take-off weight, number of passengers, range, wing area, span, fuselage length, to name a few, are likely to be available in the preliminary design stage and significantly impact the aircraft design. These variables either directly or indirectly affect the overall production cost or performance. The PCA approach includes both correlation and multiple linear regression techniques. The empirical models thus developed were able to predict the aircraft cost with an error of less than ±4% for all aircraft categories considered. Two aircraft in each defined category were not part of the PCA models and were used to verify the models. The proposed models provide the ability to estimate the aircraft cost quickly in the early stages of the preliminary design phase and provide the possibility of performing parametric studies involving the key variables to determine the cost sensitivity to the main design parameters.

In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

Helicopter collisions with obstacles are one of the most frequent and most devastating causes of accidents. To avoid these collisions in low-speed operations a “haptic ticker” cue in form of repetitive impulses as a force feedback was designed for an active sidestick. Various design questions were examined in pilot campaigns using a full flight simulator and four test scenarios. As a result, the pilots always knew which distance-based hazard area (green, yellow, red) they were in. Furthermore, the ticker is disruptive and roughly reduces the handling qualities from Level 1 to Level 2. It is therefore primarily activated as a hazard warning and not as a main input to control the distance. As a warning cue the ticker was evaluated as non-disturbing. The force threshold to detect the direction of a tick was determined. With tick strengths above this threshold, the direction is still not recognised at all in around 2% of the ticks. For the remaining ticks, the accuracy with which the direction is recognised is about 15°. In the fourth scenario, obstacles were moved towards the hovering helicopter, potentially forcing a collision. However, with the ticker a collision occurred in less than 4% of the cases, instead of 84% without the ticker. The ticker was rated as very intuitive and worth recommending. When asked how many accidents of this kind could be prevented with this ticker, all five pilots independently estimated 75%.

This paper studied the use of eye movement data to form criteria for judging whether pilots perceive emergency information such as cockpit warnings. In the experiment, 12 subjects randomly encountered different warning information while flying a simulated helicopter, and their eye movement data were collected synchronously. Firstly, the importance of the eye movement features was calculated by ANOVA (analysis of variance). According to the sorting of the importance and the Euclidean distance of each eye movement feature, the warning information samples with different eye movement features were obtained. Secondly, the residual shrinkage network modules were added to CNN (convolutional neural network) to construct a DRSN (deep residual shrinkage networks) model. Finally, the processed warning information samples were used to train and test the DRSN model. In order to verify the superiority of this method, the DRSN model was compared with three machine learning models, namely SVM (support vector machine), RF (radom forest) and BPNN (backpropagation neural network). Among the four models, the DRSN model performed the best. When all eye movement features were selected, this model detected pilot perception of warning information with an average accuracy of 90.4%, of which the highest detection accuracy reached 96.4%. Experiments showed that the DRSN model had advantages in detecting pilot perception of warning information.

Unmanned aerial vehicle (UAV) swarm coverage is one of the key technologies for multi-UAV cooperation, which plays an important role in collaborative investigation, detection, rescue and other applications. Aiming at the coverage optimisation problem of UAV in the target area, a collaborative visual coverage control method under positioning uncertainty is presented. First, the visual perception area with imprecise localisation, UAV model and sensor model are created based on the given task environment. Second, a regional division algorithm for the target task area is designed based on the principle of Guaranteed Voronoi (GV) diagram. Then a visual area coverage planning algorithm is designed, in which the task area is allocated to the UAV according to the corresponding weight coefficient of each area, and the input control law is adjusted by the expected state information of the UAV, so that the optimal coverage quality target value and the maximum coverage of the target area can be achieved. Finally, three task scenarios for regional division and coverage planning are simulated respectively, the results show that the proposed area coverage planning algorithm can realise the optimal regional distribution and can obtain more than 90% coverage in different scenarios.

The increasingly widespread application of drones and the emergence of urban air mobility leads to a challenging question in airspace modernisation: how to create a safe and scalable air traffic management system that can handle the expected density of operations. Increasing the number of vehicles in a given airspace volume and enabling routine operations are essential for these services to be economically viable. However, a higher density of operations increases risks, poses a great challenge for coordination and necessitates the development of a novel solution for traffic management. This paper contributes to the research towards such a strategy and the field of airspace management by calculating and analysing the conflict probability in an en-route, free-flight scenario for autonomous vehicles. Analytical methods are used to determine the directional dependence of conflict probabilities for exponential and normal prescribed speed probability distributions. The notions of geometric and speed conflict are introduced and distinguished throughout the calculations of the paper. The effect of truncating the set of available flight speeds is also investigated. The sensitivity of the calculated results to speed and heading perturbations is studied within the analytical framework and verified by numerical simulations. Results enable a fresh approach to conflict detection and resolution through distribution shaping and are intended to be used in an integrated, stochastic coordination framework.

Even the shortest flight through unknown, cluttered environments requires reliable local path planning algorithms to avoid unforeseen obstacles. The algorithm must evaluate alternative flight paths and identify the best path if an obstacle blocks its way. Commonly, weighted sums are used here. This work shows that weighted Chebyshev distances and factorial achievement scalarising functions are suitable alternatives to weighted sums if combined with the 3DVFH* local path planning algorithm. Both methods considerably reduce the failure probability of simulated flights in various environments. The standard 3DVFH* uses a weighted sum and has a failure probability of 50% in the test environments. A factorial achievement scalarising function, which minimises the worst combination of two out of four objective functions, reaches a failure probability of 26%; A weighted Chebyshev distance, which optimises the worst objective, has a failure probability of 30%. These results show promise for further enhancements and to support broader applicability.

Nowadays most busy international airports and their corresponding terminal areas are suffering from huge congestion issues due to the simultaneity of their arrival aircraft. The aim of this paper is to establish a new separation method using time- based-separation, speed modification during approach phases and Point Merge System (PMS) so as to ensure efficiently the traffic flow. This work took as a case study the busiest airport of Morocco, The Mohammed V International airport of Casablanca. The proposed management model offers very good results when compared with other models such as the first-come first-served (FCFS) model.

Robust control of non-linear systems is a challenging task, notably in the presence of external disturbances and uncertain parameters. The main focus of this paper is to solve the trajectory tracking problem of an unconventional quadrotor with rotating arms (also known as a foldable drone), while overcoming some of the challenges associated with this type of vehicle. Therefore, in a first step, the model of this vehicle is presented, taking into account the change of the inertia, the centre of gravity, and the control matrix. The theoretical foundations of backstepping control, based on the finite time Lyapunov stability theory and enhanced by a Super-Twisting algorithm, are then discussed. Numerical simulations are performed to demonstrate the efficiency of the suggested control approach. Finally, a qualitative and quantitative comparative study of the proposed controller with the conventional backstepping controller is performed. Overall, the obtained results show that the proposed control strategy outperforms in terms of accuracy and resilience.

From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

Prognostics and Health Management (PHM) models aim to estimate remaining useful life (RUL) of complex systems, enabling lower maintenance costs and increased availability. A substantial body of work considers the development and testing of new models using the NASA C-MAPSS dataset as a benchmark. In recent work, the use of ensemble methods has been prevalent. This paper proposes two adaptations to one of the best-performing ensemble methods, namely the Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) network developed by Li et al. (IEEE Access, 2019, 7, pp 75464–75475)). The first adaptation (adaptable time window, or ATW) increases accuracy of RUL estimates, with performance surpassing that of the state of the art, whereas the second (sub-network learning) does not improve performance. The results give greater insight into further development of innovative methods for prognostics, with future work focusing on translating the ATW approach to real-life industrial datasets and leveraging findings towards practical uptake for industrial applications.

The unstart phenomenon of supersonic inlets caused by backpressure is dangerous for aircraft during flights because it severely reduces the air mass flow rate through the engine. We used unsteady numerical simulations to evaluate the unstart and restart characteristics of a two-dimensional supersonic inlet during rapid backpressure changes. The effects of the depressurisation time and depressurisation value on the inlet flow characteristics and restart features are discussed. The results show that the depressurisation time affects the restart procedure when the back pressure drops from the inlet unstart value to the normal working state value. When the depressurisation time decreases, it becomes easier for the inlet to restart. However, the inlet cannot restart if the depressurisation time is too long. When the depressurisation time and value were large enough, a short buzz period occurred before the inlet restarted. Both the time and value of depressurisation affected the restart characteristics.

The present study employs the direct simulation Monte Carlo (DSMC) technique to analyse the flow over a cavity, a commonly observed anomaly on a re-entry vehicle’s surface. The flow characteristics are examined for different Mach numbers ( ${\rm{Ma}}$ ) and Knudsen number ( ${\rm{Kn}}$ ). The Mach numbers varied from 5 to 25, while the Knudsen numbers varied from 0.05 to 21.10. The influence of the ${\rm{Ma}}$ and ${\rm{Kn}}$ on flow characteristics has been elucidated graphically in various sections. The flow properties showed significant variation with ${\rm{Ma}}$ and ${\rm{Kn}}$ and showed an increasing trend due to compressibility and viscous heating effects. The surface characteristics were observed to diminish as ${\rm{Ma}}$ increases, while they showed complex trends for various ${\rm{Kn}}$ . In all flow regimes, there was an appearance of flow recirculation. When chemical reactions were taken into consideration and compared with non-reacting flows, flow temperature was primarily influenced (which decreased due to energy absorption) compared to other properties. The results obtained are a complex interplay of the viscous heating, compression and rarefaction effects.

A fast numerical method for unsteady aerodynamic calculation of 3D wing is established, which is suitable for the preliminary design. Based on the lifting-line method, the aerodynamic data of the 2D aerofoil obtained by the unsteady CFD simulation is used as the model input to solve the aerodynamic force of the 3D wing. Compared with the traditional steady lifting-line method, the augmented method adopts the unsteady Kutta-Jouowski (K-J) theorem to calculate the circulation and improve the accuracy of the method through the circulation correction. The pitching motion of 3D wing at different aspect ratio and reduction frequencies are studied. The results show that the aerodynamic forces obtained by the augmented lifting-line method have good agreement with the 3D unsteady CFD calculations. Compared with 3D CFD calculation, the calculation efficiency of the improved method is increased by more than 12 times. The improved method has extensive applicability and can be used to estimate the unsteady aerodynamic forces of 3D single or multiple wing configurations.

Aerodynamic characterisation from flight testing is an integral subroutine for evaluating a new flight vehicle’s aerodynamic performance, stability and controllability. The estimation of aerodynamic parameters from flight test data has extensively been explored, in the past, using estimation methods such as the equation error method, output error method and filter error method. However, in the current era, non-gradient-based estimation techniques are gaining attention from researchers due to their inherent data-driven optimisation capability to find the global best solution. In this paper, a novel non-gradient-based estimation method is proposed for the aerodynamic characterisation of unmanned aerial vehicles from flight data, which relies on the maximum likelihood method augmented with particle swarm optimisation. Flight data sets of a wing-alone unmanned aerial vehicle are used to demonstrate the capabilities of the proposed method in estimating aerodynamic derivatives. Estimates from the proposed method are corroborated with the wind tunnel test and output error method results. It has been observed that simulated flight vehicle responses using estimated parameters are in good agreement with measured data in most of the manoeuvers considered. Confidence in the estimates of linear and nonlinear aerodynamic parameters is well established with the lower limit of Cramer-Rao bounds, which are minimal. The proposed method also demonstrates good predictability of the quasi-steady stall aerodynamic model by estimating stall characteristic parameters such as aerofoil static stall characteristics parameter, hysteresis time constant and breakpoint. The overall performance of the proposed estimation method is on par with the output error method and is validated with the proof-of-match exercise.

Whirl flutter is an aeroelastic instability that affects aircraft with propellers/rotors. With their long and flexible rotor blades, tiltrotor aircraft are particularly susceptible. Whirl flutter is known to have destroyed aircraft and in the best case it constitutes a fatigue hazard. The complexity of whirl flutter analysis increases significantly with the addition of nonlinearities, due to the more complex dynamical behaviours that emerge as a result. Most whirl flutter stability analyses in current literature are grounded in linear theory, preventing the full discovery of the nonlinearities’ effects. Continuation and bifurcation methods (CBM) may instead be used to fully appreciate and analyse the effects of the presence of nonlinearities. Previous CBM-based work on nonlinear gimballed hub rotor-nacelle models, representing those found on tiltrotor aircraft, are capable of whirl flutter in parametric regions declared safe by linear analysis. Furthermore, it was found that they are capable of complex behaviours including limit cycle oscillations, quasi-periodic behaviour and even chaos, though the whirl flutter implications of such behaviours has not been explored. This paper investigates the impact of a smooth structural nonlinearity on the whirl flutter stability of a basic gimballed rotor-nacelle model, compared to its baseline linear stiffness version. A 9-DoF model with quasi-steady aerodynamics, a flexible wing and blades that can move both cyclically and collectively in both flapping and lead-lag motions, producing gimbal flap-like behaviour, was adopted from existing literature. A smooth stiffness nonlinearity was introduced in the blade flapping stiffness and CBM was used to find the new whirl flutter behaviours created by the presence of the nonlinearity. Time simulations, Poincaré sections and spectral analysis were then used to investigate the various behaviours found. This in turn allowed recommendations to be made concerning preferable and/or hazardous parameter combinations of use to the tiltrotor designer.

For a hypersonic-speed aircraft with a flat fuselage structure that has narrow space for a traditional wheel-type landing gear retraction, a novel type of wheel-ski landing gear is designed, which is different from traditional landing gears in force distribution and actuation methods. In order to capture the direction control performance of an aircraft with the wheel-ski landing gear, the aircraft ground taxiing nonlinear dynamic mathematical model is built based on a certain type of aircraft data. The experiment of the wheel-ski landing gear actuator and the differential brake control system is carried out to verify that the electric wheel-ski actuator model with the pressure sensor is in good agreement with the test results, indicating the model validity and the speediness of the differential brake response. Then a new fuzzy combined direction rectifying control law is designed based on the optimisation method and the fuzzy control theory. Comparing with the PD wheel-ski differential brake control, the direction rectifying efficiencies increase higher than 140% during the whole taxiing process. In addition, the combined control law can also decrease the overshoots of the yaw angle responses effectively. Finally, the stability and robustness of the designed combined direction control law are verified under various working conditions.

The complex and iterative workflow for designing the secondary air system (SAS) of a gas turbine engine still largely depends on human expertise and hence requires long lead times and incurs high design time-cost. This paper proposes an automated methodology to generate the whole-engine SAS flow network model from the engine geometry model and presents a convenient and inter-operable framework of the secondary air system modeller. The SAS modeller transforms the SAS cavities and flow paths into a 1D flow network model composed of nodes and links. The novel, object-oriented pre-processor embedded in the SAS modeller automatically assembles the conservation equations for all flow nodes and the loss correlations for all links. The twin-level, hierarchical SAS solver then solves the conservation equations of mass, momentum and energy supplemented with the correlations in the loss model library. The modelling swiftness, mathematical robustness and numerical stability of the present methodology are demonstrated through the results obtained from IP compressor rotor drum flow network model.