The reduction of computational costs in the context of the Multidisciplinary Design Optimisation of a typical medium-range aircraft was investigated through an assessment of active constraints and the use of multi-fidelity models-based estimation of drag and structural stress. The results show that for this problem, from the set of considered constraints that includes flutter boundary, the active constraint is a 2.5g pull up Maximum Take Off Weight. Results show that the multi-fidelity approach reduced the required high-fidelity aerodynamic number of evaluations, for both drag assessment and stress assessment with sufficient level of accuracy for the former and conservatively for the latter. Further computational cost reduction can be achieved using a surrogate model based Multidisciplinary Design Optimisation. The best configuration attained shows an Aspect Ratio increase of 16%, a reduction of 4.5% in fuel consumption and wing structural weight increase of 2.7% relative to a predefined baseline configuration.

Flutter suppression is an important measure to improve fatigue life and enhance the performance of aircraft in modern aircraft design. In order to design more effective controllers for flutter suppression with high efficiency, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed. The traditional CFD-based ROMs have been successfully applied to active flutter suppression with high accuracy and efficiency. But, when a structure modification is made such as in aeroelastic tailoring and aeroelastic structural optimisation, the structural model should be updated, and the expensive, time-consuming CFD-based ROMs have to be reconstructed; such a process is impractical for passive flutter suppression. To overcome the realistic challenge, an efficient reduced-order framework for active/passive hybrid flutter suppression is proposed by extending an efficient aeroelastic CFD-based POD/ROM which we have developed. The proposed framework is demonstrated and evaluated using an improved AGARD 445.6 wing model. The results show that the proposed framework can accurately predict the aeroelastic response for active/passive hybrid flutter suppression with high efficiency. It provides a powerful tool for active/passive hybrid flutter suppression, and therefore, is ideally suited to design more effective controllers, and may have the potential to reduce the overall cost of aircraft design.

A control system verification framework is presented for unmanned aerial vehicles using theorem proving. The framework’s aim is to set out a procedure for proving that the mathematically designed control system of the aircraft satisfies robustness requirements to ensure safe performance under varying environmental conditions. Extensive mathematical derivations, which have formerly been carried out manually, are checked for their correctness on a computer. To illustrate the procedures, a higher-order logic interactive theorem-prover and an automated theorem-prover are utilised to formally verify a nonlinear attitude control system of a generic multi-rotor UAV over a stability domain within the dynamical state space of the drone. Further benefits of the procedures are that some of the resulting methods can be implemented onboard the aircraft to detect when its controller breaches its flight envelope limits due to severe weather conditions or actuator/sensor malfunction. Such a detection procedure can be used to advise the remote pilot, or an onboard intelligent agent, to decide on some alterations of the planned flight path or to perform emergency landing.

In this paper, to address the cooperative localisation of a heterogeneous UAV swarm in the GNSS-denied environment, an adaptive simulated annealing-particle swarm optimisation (SA-PSO) cooperative localisation algorithm is proposed. Firstly, the forming principle of the communication and measurement framework is investigated in light of a heterogeneous UAV swarm composition. Secondly, a reasonably cooperative localisation function is established based on the proposed forming principle, which can minimise the relative localisation error with limited available information. Then, an adaptive weight principle is incorporated into the particle swarm optimisation (PSO) for better performance. Furthermore, in order to overcome the drawbacks of PSO algorithm easily falling into the local extreme point, an adaptive SA-PSO algorithm is improved to promote the convergence speed of cooperative localisation. Finally, comparative simulations are performed among the adaptive SA-PSO, adaptive PSO, and PSO algorithms to demonstrate the feasibility and superiority of the proposed adaptive SA-PSO algorithm. Simulation results show that the proposed algorithm has better performance in convergence speed, and the cooperative localisation precision can be guaranteed.

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.

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.

Unmanned Aerial Vehicles (UAVs) have been used extensively in many applications, such as surveillance, medical transportation and delivery tasks. These applications usually involve an attachment system to connect a payload. However, when connected to such a coupled system, the UAV exhibits lower flight performance mainly due to gust loads. Mathematical modeling is used in this paper to illustrate the comparison between an UAV with a coupled system and a conventional UAV. Proportional Derivative (PD) and Sliding Mode Control (SMC) approaches are used to evaluate the vibrations of the payload, and the UAV trajectory subjected to Dryden gust. This work therefore uses these methods to investigate the dynamic response of a quadrotor-type UAV equipped with a payload and a flexible attachment system.

This paper concentrates on the trajectory tracking problem for a stratospheric airship subject to underactuated dynamics, unmeasured velocities, modeling inaccuracies and environmental disturbances. First, a coordinate transformation is performed to solve the underactuated issue, which simultaneously permits a priori assignment of the tracking accuracy. Second, a finite-time observer is integrated into the control structure to offer the exact information of unmeasured velocities and uncertainties in an integral manner. Then, by combining the backstepping technique with the method of adding a power integrator, a new output-feedback control strategy is derived with several salient contributions: (1) the airship’s position errors fall into a predetermined residual region near zero within a finite settling time and stay there, while all the closed-loop signals maintain bounded during operation; and (2) no artificial neural networks and filters are adopted, resulting in a low-complexity control property. Furthermore, the presented method can be extended readily to a broad range of second-order mechanical systems as its design builds upon a transformed system model. Rigorous mathematical analysis and simulations demonstrate the above theoretical findings.