A new type of design domain for a sounding rocket is suggested in this study, which is more intuitive, simplified, and conducive to a single-stage sounding rocket development process. Among the various operation parameters, this study identifies several effective variables, which are also among the most practical in a sounding rocket design process. Of the many design variables considered for peak altitude optimisation, arguably the three most effective in shaping the whole system and conferring the most practicality in the design process of a sounding rocket were determined. A simulation-based study was conducted to establish: the effect of the selected parameters on flight performance, and the optimum design conditions of a single-stage sounding rocket in terms of its peak altitude. The simulation result was compared with randomly chosen experimentally tested flight data and validated. With the clear performance curves varying depending on the variables, the combination of the considered design inputs was effective. The new type of design domain and design procedure proposed is expected to constitute a useful reference and deliver practical benefits to the development process for a target-altitude-optimised single-stage sounding rocket.

Reinforcement learning has previously been applied to the problem of controlling a perched landing manoeuvre for a custom sweep-wing aircraft. Previous work showed that the use of domain randomisation to train with atmospheric disturbances improved the real-world performance of the controllers, leading to increased reward. This paper builds on the previous project, investigating enhancements and modifications to the learning process to further improve performance, and reduce final state error. These changes include modifying the observation by adding information about the airspeed to the standard aircraft state vector, employing further domain randomisation of the simulator, optimising the underlying RL algorithm and network structure, and changing to a continuous action space. Simulated investigations identified hyperparameter optimisation as achieving the most significant increase in reward performance. Several test cases were explored to identify the best combination of enhancements. Flight testing was performed, comparing a baseline model against some of the best performing test cases from simulation. Generally, test cases that performed better than the baseline in simulation also performed better in the real world. However, flight tests also identified limitations with the current numerical model. For some models, the chosen policy performs well in simulation yet stalls prematurely in reality, a problem known as the reality gap.

In order to intercept a highly manoeuvering target with an ideal impact angle in the three-dimensional space, this paper promises to probe into the problem of three-dimensional terminal guidance. With the goal of the highly target acceleration and short terminal guidance time, a guidance law, based on the advanced fast non-singular terminal sliding mode theory, is designed to quickly converge the line-of-sight (LOS) angle and the LOS angular rate within a finite time. In the design process, the target acceleration is regarded as an unknown boundary external disturbance of the guidance system, and the RBF neural network is used to estimate it. In order to improve the estimation accuracy of RBF neural network and accelerate its convergence, the parameters of RBF neural network are adjusted online in real time. At the same time, an adaptive law is designed to compensate the estimation error of the RBF neural network, which improves the convergence speed of the guidance system. Theoretical analysis demonstrates that the state and the sliding manifold of the guidance system converge in finite time. According to Lyapunov theory, the stability of the system can be guaranteed by online adjusting the parameters of RBF neural network and adaptive parameters. The numerical simulation results verify the effectiveness and superiority of the proposed guidance law.

This article focuses on the aerodynamic design of a morphing aerofoil at cruise conditions using computational fluid dynamics (CFD). The morphing aerofoil has been analysed at a Mach number of 0.8 and Reynolds number of $3 \times 10^{6}$ , which represents the transonic cruise speed of a commercial aircraft. In this research, the NACA0012 aerofoil has been identified as the baseline aerofoil where the analysis has been performed under steady conditions at a range of angles of attack between $0^{^{\kern1pt\circ}}$ and $3.86^{^{\kern1pt\circ}}$ . The performance of the baseline case has been compared to the morphing aerofoil for different morphing deflections ( $w_{te}/c = [0.005 - 0.1]$ ) and start of the morphing locations ( $x_{s}/c = [0.65 - 0.80]$ ). Further, the location of the shock wave on the upper surface has also been investigated due to concerns about the structural integrity of the morphing part of the aerofoil. Based upon this investigation, a most favourable morphed geometry has been presented that offers both, a significant increase in the lift-to-drag ratio against its un-morphed counterpart and has a shock location upstream of the start of the morphing part.

The rotorcraft is a complex dynamical system that demands specialist modelling skills, and a high level of understanding of the aeromechanics arising from the main rotor wake and aerodynamic couplings. One such example is the difficulty predicting off-axis responses, particularly in hover and low-speed flight, associated with induced velocity variation through the rotor disk resulting from the rotor wake distortions. Various approaches have been developed to deal with this phenomenon but usually demand prerequisites of high levels of expertise and profound aerodynamic knowledge. This paper presents a new and practical approach to capturing this wake distortion through an augmented rotor inflow model. The proposed model is coupled with a nonlinear simulation using the FLIGHTLAB environment, and comparisons are made between the simulation results and flight test data from the National Research Council of Canada’s Advanced System Research Aircraft in hover and low speed. Results show good predictability of the proposed nonlinear model structure, demonstrated by its capability to closely match the time responses to multi-step control inputs from flight test. The results reported are part of ongoing research at Liverpool and Cranfield University into rotorcraft simulation fidelity.

In tandem with the fast-growing demand for Unmanned Aerial Vehicles (UAVs) for surveillance and reconnaissance, advanced controllers for these critical systems are needed. This paper proposes a flight dynamics controller design that considers various uncertainties for the Hydra Technologies UAS-S4 Ehécatl. In order to be realistic, in addition to flight dynamics nonlinearities, three main sources of uncertainties are considered, as those caused by unknown controller’s parameters, modeling errors, and external disturbances. A Robust adaptive fuzzy logic controller is designed, in charge of nonlinear flight dynamics in presence of a variety of uncertainties. The nonlinear flight dynamics is modeled based on the Takagi-Sugeno method relying on the soft association of local linear models. Since this controller is model-based, an optimal reference model is defined, which is stabilised by the Linear Quadratic Regulator procedure. A fuzzy logic controller is then designed for the nonlinear model. Lastly, with the aim to handle the uncertainties, the gains of the fuzzy controller are reconfigured, and are continuously adjusted by Lyapunov-based robust adaptive laws. The performance of the UAS-S4 Robust adaptive fuzzy logic controller is evaluated in terms of lateral and longitudinal flight dynamics stabilisation, and the reference model state variables tracking under various uncertainties.

In recent years, Performance Based Logistics (PBL) has been increasingly used to reduce the product lifecycle cost. As a result, the existing logistics analysis methods need to be reassessed due to the difference in PBL from the classical approach. This study considers the Spare Parts Allocation (SPA) and Level of Repair Analysis (LORA), which are the most commonly used problems within PBL, and are the subject of analysis. A comprehensive, multi-objective simulation-optimisation model is developed for a military aircraft operations case study, with the objectives of minimising total cost and maximising total flight hours. The Design of Experiment (DOE) method is used for determining simulation-optimisation parameters. Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for the first time in order to select the best design points. Simulated Annealing (SA) was used for multi-objective optimisation with the SPA model being solved first. Then the LORA problem was added to this model as a decision variable and the effects of the integrated solution (SPA+LORA) were examined. The results show that the integrated solution yields remarkably better results in terms of flight hours and cost when compared to the SPA optimisation approach alone. Moreover, the proposed model provides a profit-centric approach and can be efficiently used as a decision support tool for both customers and suppliers for difficult and complex logistics support activities.