In this paper, we examine some of the physics behind Vertical Take-Off and Landing (VTOL) flying machines, and some of the emerging technologies that are driving the recent upsurge of new VTOL projects. The paper attempts to put these into context by examining some of the projects that have been publicised over the past couple of years, particularly those that transition from hovering into wing-borne flight. Although much progress has been made, there still needs to be significant breakthroughs in technologies, particularly battery technology, before the dream of fast, quiet and environmentally friendly inter-city VTOL aircraft can be realised.

This paper deals with the problem of fault-tolerant control (FTC) for redundant multirotor unmanned aerial vehicles (UAVs) subject to actuators failures. A fuzzy logic approach is used to solve the constrained control allocation problem by adjusting the components of the multiplexing vector once a motor failure is detected. This fuzzy logic allocation problem is tuned using the Bacterial Foraging Algorithm (BFA), a powerful bio-inspired optimisation technique. The effectiveness of this approach is illustrated through real experimental application to a hexarotor UAV, where up to two motors failures are considered.

The wake vortex of lifting surfaces such as wind turbine blades or fixed-wing aircraft can heavily affect the blade aerodynamics of rotorcraft. Using blade element theory, the pilot control inputs required to mitigate such vortex effects are estimated and compared to the available control margin at the operating condition of interest. In contrast, when no pilot action is performed, the rotor blade flapping caused by the vortex is evaluated and compared to available margins. It is a safety concern when the remaining margins become zero. The influence of the vortex strength, its core radius and orientation to the rotor disk are evaluated and the effect of rotor blade characteristics (Lock number, natural frequency) is investigated.

In order to increase the speed, precision and robustness against the engine failure in solving optimal endo-atmospheric ascent trajectory of a launch vehicle, a rapid multi-layer solving method with improved numerical algorithms was proposed. The proposed method is capable of decomposing a large number of intervals into multiple layers with advantageous convergence property. Firstly, the problem of solving optimal endo-atmospheric ascent trajectory, which was subjected to path constraints and terminal constraints, was transformed into a Hamilton Two Point Boundary Value Problem (TPBVP). Then, through the finite difference method and numerical solving algorithm, the Hamilton TPBVP was iteratively solved with fewer initial discrete intervals. The initial values of higher-layer iterations were obtained by interpolating convergent solutions at sparse nodes into the doubly discrete nodes of high layers. The process was repeatedly performed until the solving precision met the requirements. To decrease the calculation load in solving TPBVPs, two improved solving algorithms without and with fewer Jacobian calculations were studied, respectively the Derivative-free Spectral Algorithm for Nonlinear Equations(DF-SANE) combined with the improved derivative-free nonmonotone line search strategy, and the Modified Newton method with a relaxation factor in combination with the Inverse Broyden Quasi-Newton method, denoted as ‘MN-IBQ’. Simulation verifications showed that the multi-layer method had significantly higher solving speed than the single-layer method. For the improved numerical algorithms, the DF-SANE was trapped in the local convergence problem. While using the proposed MN-IBQ can further increase the solving rate. Typical engine failure simulations showed that the multi-layer method with the MN-IBQ algorithm had not only significantly higher solving speed but also stronger robustness, where the traditional single-layer method could not adapt. In addition, the thrust loss tolerance limits for the multi-layer solving method were given for different engine failure times. The results show promising potential of the proposed approach in trajectory online generation and closed-loop guidance of launch vehicles at the endo-atmospheric ascent stage.

Data mining is a process of finding correlations and collecting and analysing a huge amount of data in a database to discover patterns or relationships. Flight delay creates significant problems in the present aviation system. Data mining techniques are desired for analysing the performance in which micro-level causes propagate to make system-level patterns of delay. Analysing flight delays is very difficult – both when looking from a historical view as well as when estimating delays with forecast demand. This paper proposes using Decision Tree (DT), Support Vector Machine (SVM), Naive Bayesian (NB), K-nearest neighbour (KNN) and Artificial Neural Network (ANN) to study and analyse delays among aircrafts. The performance of different data mining methods is found in the different regions of the updated datasets on these classifiers. Finally, the result shows a significant variation in the performance of different data mining methods and feature selection for this problem. This paper aims to deal with how data mining techniques can be used to understand difficult aircraft system delays in aviation. Our aim is to develop a classification model for studying and reducing delay using different data mining methods and, in this manner, to show that DT has a greater classification accuracy. The different feature selectors are used in this study in order to reduce the number of initial attributes. Our results clearly demonstrate the value of DT for analysing and visualising how system-level effects happen from subsystem-level causes.

This paper considers the integrated guidance and control (IGC) problem for impact angle constrained interception against manoeuvring targets with actuator saturation constraint. Based on the backstepping technique, an adaptive IGC law is presented to address this problem, where a fixed-time differentiator is proposed to estimate the derivatives of virtual control inputs to avoid the inherent problem of “explosion of complexity” suffered by the typical backstepping. Furthermore, an auxiliary first-order filter is introduced into the IGC law to cope with the actuator saturation constraint. The stability of the closed-loop system is strictly proved. Finally, the superiority of the proposed IGC law is verified by comparison simulations.