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.

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.

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.

Weapon target allocation (WTA) is an effective method to solve the battlefield fire optimisation problem, which plays an important role in intelligent automated decision-making. We researched the multitarget allocation problem to maximise the attack effectiveness when multiple interceptors cooperatively attack multiple ground targets. Firstly, an effective and reasonable fitness function is established, based on the situation between the interceptors and targets, by comprehensively considering the relative range, relative angle, speed, capture probability and radiation source matching performance and thoroughly evaluating them based on the advantage of the attack effectiveness. Secondly, the optimisation performance of the particle swarm optimisation (PSO) algorithm is adaptively improved. We propose an adaptive simulated annealing-particle swarm optimisation (SA-PSO) algorithm by introducing the simulated annealing algorithm into the adaptive PSO algorithm. The proposed algorithm can enhance the convergence speed and overcome the disadvantage of the PSO algorithm easily falling into a local extreme point. Finally, a simulation example is performed in a scenario where ten interceptors cooperate to attack eight ground targets; comparative experiments are conducted between the adaptive SA-PSO algorithm and PSO algorithm. The simulation results indicate that the proposed adaptive SA-PSO algorithm demonstrates great performance in convergence speed and global optimisation capabilities, and a maximised attack effectiveness can be guaranteed.

The design and development of Stratospheric Airships for High-Altitude Long-Endurance missions (HALESAs) has generated interest worldwide. Conventional airships usually have a single-lobed axisymmetrical envelope shape. In contrast, several non-axisymmetric envelope configurations have been proposed for the HALESAs, such as flattened single lobed and multi lobed. This paper describes a methodology for carrying out a comparative analysis of a conventional HALESA and the multi-lobed HALESA designed for the same design mission. A sizing methodology which enables the estimation of its design parameters to meet some user-specified requirements has been developed for airships with envelopes of both these shapes. A Multidisciplinary Design Optimisation (MDO) approach has been followed in this methodology, which includes considerations from the disciplines of aerodynamics, energy, environment and structures. The study indicates that the envelope volume, solar array area and total mass of the single-lobed conventional airship are better than those of the tri-lobed HALESA. While the multi-lobed HALESA has the advantage of a flatter upper surface resulting in higher efficiency of the solar panels, the conventional airship has lower drag, which results in superior mission performance.

Recent research on Flight-deck Interval Management (FIM), a modern technology for increasing safety and improving airspace and runway utilisation through self-spacing, has led to the development of a new rule-based logic for FIM, namely Interval Management – Speed Planning (IM-SP). In an initial benchmark study, IM-SP showed good spacing performance with a significant reduction in speed commands, a major area of concern with previous FIM logics, resulting in a lower burden on the flight crew during FIM operation. Nevertheless, there remains scope for improvement in other aspects, such as fuel burn. In this study, the internal cost function of IM-SP is further analysed and optimised using speed-constrained multi-objective particle swarm optimisation to improve the performance of IM-SP under the multiple objectives of FIM. The optimisation renders new settings that address the problem areas, improve the speed commands and enhance the overall quality of IM-SP. Two distinctive solutions, viz. a spacing performance optimised setting and a fuel burn optimised setting, are further analysed and discussed, and directions for follow-up research are explored.

The paper presents the design and experimental testing of the control system used in a new morphing wing application with a full-scaled portion of a real wing. The morphing actuation system uses four similar miniature brushless DC (BLDC) motors placed inside the wing, which execute a direct actuation of the flexible upper surface of the wing made from composite materials. The control system of each actuator uses three control loops (current, speed and position) characterised by five control gains. To tune the control gains, the Particle Swarm Optimisation (PSO) method is used. The application of the PSO method supposed the development of a MATLAB/Simulink® software model for the controlled actuator, which worked together with a software sub-routine implementing the PSO algorithm to find the best values for the five control gains that minimise the cost function. Once the best values of the control gains are established, the software model of the controlled actuator is numerically simulated in order to evaluate the quality of the obtained control system. Finally, the designed control system is experimentally validated in bench tests and wind-tunnel tests for all four miniature actuators integrated in the morphing wing experimental model. The wind-tunnel testing treats the system as a whole and includes, besides the evaluation of the controlled actuation system, the testing of the integrated morphing wing experimental model and the evaluation of the aerodynamic benefits brought by the morphing technology on this project. From this last perspective, the airflow on the morphing upper surface of the experimental model is monitored by using various techniques based on pressure data collection with Kulite pressure sensors or on infrared thermography camera visualisations.

For the integrated navigation system, the correctness and the rapidity of fault detection for each sensor subsystem affects the accuracy of navigation. In this paper, a novel fault detection method for navigation systems is proposed based on Gaussian Process Regression (GPR). A GPR model is first used to predict the innovation of a Kalman filter. To avoid local optimisation, particle swarm optimisation is adopted to find the optimal hyper-parameters for the GPR model. The Fault Detection Function (FDF), which has an obvious jump in value when a fault occurs, is composed of the predicted innovation, the actual innovation of the Kalman filter and their variance. The fault can be detected by comparing the FDF value with a predefined threshold. In order to verify its validity, the proposed method is used in a SINS/GPS/Odometer integrated navigation system. The comparison experiments confirm that the proposed method can detect a gradual fault more quickly compared with the residual chi-squared test. Thus the navigation system with the proposed method gives more accurate outputs and its reliability is greatly improved.

The use of active control techniques has intensified in various control applications, particularly in the field of aircraft systems. This paper presents an investigation into the control of rigid-body and flexible motion of a twin rotor multi-input multi-output system (TRMS) using intelligent inverse-model-based control schemes. The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. The augmented feedback PID and feedforward inverse-model-based control has led to good tracking response and vibration reduction of the TRMS, with the use of particle swarm optimisation (PSO). As a comparison, methods using PID controllers are also presented. Experimental results are obtained using the test rig, confirming the viability and effectiveness of the proposed methodology as opposed to conventional PID controllers. The results and evidence from this method are justified, presented and discussed.

This paper builds on prior research into the application of particle swarm optimisation to autonomous vehicle control in search roles. It examines the use of naturally inspired search strategies to enhance the performance of groups of sensor-based vehicles in applications where there is no knowledge a priori regarding target presence, location, distribution or behaviour (movement). This paper first briefly reviews existing ethological research into search strategies in the natural world, identifying three types of random walk, two multi-phase strategies and two species-specific strategies for further investigation. Experiments are then performed within a simulation environment to compare the performance of naturally inspired strategies with deterministic patterns and random movement, when searching for both static and dynamic targets. Results indicate that performance improvements can be realised, provided that critical relationships within the application domain broadly match those existing in the underlying natural metaphor.

This work investigates the utilisation of Particle Swarm Optimisation (PSO) for the non-deterministic navigation of Unmanned Aerial Vehicles (UAVs), allowing them to work cooperatively toward the goal of protecting a wide area against airborne attack. To negate the PSO's inherent weakness in dynamic environments, a neighbourhood scheme is proposed that not only enables the efficient interception of targets several times faster than the UAVs but also facilitates the maintenance of effective airspace coverage. Empirical results suggest that these techniques may indeed be of use in autonomous navigation systems for UAVs in air defence roles.