Application of velocity-based gain-scheduling to lateral auto-pilot design for an agile missile

In this paper a modern gain-scheduling methodology is proposed which exploits recently developed velocity-based techniques to resolve many of the deficiencies of classical gain-scheduling approaches (restriction to near equilibrium operation, to slow rate of variation). This is achieved while maintaining continuity with linear methods and providing an open design framework (any linear synthesis approach may be used) which supports divide and conquer design strategies. The application of velocity-based gain-scheduling techniques is demonstrated in application to a demanding, highly nonlinear, missile control design task. Scheduling on instantaneous incidence (a rapidly varying quantity) is well-known to lead to considerable difficulties with classical gain-scheduling methods. It is shown that the methods proposed here can, however, be used to successfully design an effective and robust gain-scheduled controller

Modelling and Verification of Multiple UAV Mission Using SMV

Model checking has been used to verify the correctness of digital circuits, security protocols, communication protocols, as they can be modelled by means of finite state transition model. However, modelling the behaviour of hybrid systems like UAVs in a Kripke model is challenging. This work is aimed at capturing the behaviour of an UAV performing cooperative search mission into a Kripke model, so as to verify it against the temporal properties expressed in Computation Tree Logic (CTL). SMV model checker is used for the purpose of model checking

Airborne mapping of complex obstacles using 2D Splinegon

This paper describes a recently proposed algorithm in mapping the unknown obstacle in a stationary environment where the obstacles are represented as curved in nature. The focus is to achieve a guaranteed performance of sensor based navigation and mapping. The guaranteed performance is quantified by explicit bounds of the position estimate of an autonomous aerial vehicle using an extended Kalman filter and to track the obstacle so as to extract the map of the obstacle. This Dubins path planning algorithm is used to provide a flyable and safe path to the vehicle to fly from one location to another. This description takes into account the fact that the vehicle is made to fly around the obstacle and hence will map the shape of the obstacle using the 2D-Splinegon technique. This splinegon technique, the most efficient and a robust way to estimate the boundary of a curved nature obstacles, can provide mathematically provable performance guarantees that are achievable in practice

Velocity-Based Gain-Scheduled Lateral Auto-Pilot For An Agile Missile

This paper investigates the application of velocity-based gain-scheduling techniques to a demanding, highly nonlinear, missile control design task. Scheduling on instantaneous incidence (a rapidly varying quantity) is well known to lead to considerable difficulties with conventional gain-scheduling methods. Moreover, the missile dynamics do not have well-defined relative degree at operating points where the lateral velocity is zero and thus conventional feedback linearisation methods cannot be applied to design a controller

Autonomous systems thermographic NDT of composite structures

Transient thermography is a method used successfully in the evaluation of composite materials and aerospace structures. It has the capacity to deliver both qualitative and quantitative results about hidden defects or features in a composite structure. Aircraft must undergo routine maintenance – inspection to check for any critical damage and thus to ensure its safety. This work aims to address the challenge of NDT automated inspection and improve the defects’ detection by suggesting an autonomous thermographic imaging approach using a UAV (Unmanned Aerial Vehicle) active thermographic system. The concept of active thermography is discussed and presented in the inspection of aircraft CFRP panels along with the mission planning for aerial inspection using the UAV for real time inspection. Results indicate that the suggested approach could significantly reduce the inspection time, cost, and workload, whilst potentially increase the probability of detection of defects on aircraft composites

Application of NDT thermographic imaging of aerospace structures

This work aims to address the effectiveness and challenges of Non-Destructive Testing (NDT) inspection and improve the detection of defects without causing damage to the material or operator. It focuses on two types of NDT methods; pulsed thermography and vibrothermography. The paper also explores the possibility of performing automated aerial inspection using an unmanned aerial vehicle (UAV) provided with a thermographic imaging system. The concept of active thermography is discussed for inspecting aircraft CFRP panels along with the proposal for performing aerial inspection using the UAV for real time inspection. Static NDT results and the further UAV research indicate that the UAV inspection approach could significantly reduce the inspection time, cost, and workload, whilst potentially increasing the probability of detection

Robust control of quasi-linear parameter-varying L2 point formation flying with uncertain parameters

Robust high precision control of spacecraft formation flying is one of the most important techniques required for high-resolution interferometry missions in the complex deep-space environment. The thesis is focussed on the design of an invariant stringent performance controller for the Sun-Earth L2 point formation flying system over a wide range of conditions while maintaining system robust stability in the presence of parametric uncertainties. A Quasi-Linear Parameter-Varying (QLPV) model, generated without approximation from the exact nonlinear model, is developed in this study. With this QLPV form, the model preserves the transparency of linear controller design while reflecting the nonlinearity of the system dynamics. The Polynomial Eigenstructure Assignment (PEA) approach used for Linear Time-Invariant (LTI) and Linear Parameter-Varying (LPV ) models is extended to use the QLPV model to perform a form of dynamic inversion for a broader class of nonlinear systems which guarantees specific system performance. The resulting approach is applied to the formation flying QLPV model to design a PEA controller which ensures that the closed-loop performance is independent of the operating point. Due to variation in system parameters, the performance of most closed-loop systems are subject to model uncertainties. This leads naturally to the need to assess the robust stability of nonlinear and uncertain systems. This thesis presents two approaches to this problem, in the first approach, a polynomial matrix method to analyse the robustness of Multiple-Input and Multiple-Output (MIMO) systems for an intersectingD-region,which can copewith time-invariant uncertain systems is developed. In the second approach, an affine parameterdependent Lyapunov function based Linear Matrix Inequality (LMI) condition is developed to check the robust D-stability of QLPV uncertain systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Path planning of multiple autonomous vehicles

Safe and simultaneous arrival of constant speed, constant altitude UAVs on target is solved by design of paths of equal lengths. The starting point of the solution is the well-known Dubins path which is composed of circular arcs and line segments, thus requiring only one simple manoeuvre - constant rate turn. An explicit bound can be imposed on the rate during the design and the resulting paths are the minimum time solution of the problem. However, transition between arc and line segment entails discontinuous changes in lateral accelerations (latax), making this approach impractical for real fixed wing UAVs. Therefore, the Dubins solution is replaced with clothoid and also a novel one, based on quintic Pythagorean Hodograph (PH) curves, whose latax demand is continuous. The clothoid solution is direct as in the case of the Dubins path. The PH path is chosen for its rational functional form. The clothoid and the PH paths are designed to have lengths close to the lengths of the Dubins paths to stay close to the minimum time solution. To derive the clothoid and the PH paths that way, the Dubins solution is first interpreted in terms of Differential Geometry of curves using the path length and curvature as the key parameters. The curvature of a Dubins path is a piecewise constant and discontinuous function of its path length, which is a differential geometric expression of the discontinuous latax demand involved in transitions between the arc and the line segment. By contrast, the curvature of the PH path is a fifth order polynomial of its path length. This is not only continuous, also has enough design parameters (polynomial coefficients) to meet the latax (curvature) constraints (bounds) and to make the PH solution close to the minimum time one. The offset curves of the PH path are used to design a safety region along each path. The solution is simplified by dividing path planning into two phases. The first phase produces flyable paths while the second phase produces safe paths. Three types of paths are used: Dubins, clothoid and Pythagorean Hodograph (PH). The paths are produced both in 2D and 3D. In two dimensions, the Dubins path is generated using Euclidean and Differential geometric principles. It is shown that the principles of Differential geometry are convenient to generalize the path with the curvature. Due to the lack of curvature continuity of the Dubins path, paths with curvature continuity are considered. In this respect, initially the solution with the Dubins path is extended to produce clothoid path. Latter the PH path is produced using interpolation technique. Flyable paths in three dimensions are produced with the spatial Dubins and PH paths. In the second phase, the flyable paths are tuned for simultaneous arrival on target. The simultaneous arrival is achieved by producing the paths of equal lengths. Two safety conditions: (i) minimum separation distance and (ii) non-intersection of paths at equal distance are defined to maneuver in free space. In a cluttered space, an additional condition, threat detection and avoidance is defined to produce safe paths. The tuning is achieved by increasing the curvature of the paths and by creating an intermediate way-point. Instead of imposing safety constraints, the flyable paths are tested for meeting the constraints. The path is replanned either by creating a new way-point or by increasing the curvature between the way-points under consideration. The path lengths are made equal to that of a reference path.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Trajectory generation for autonomous unmanned aircraft using inverse dynamics

The problem addressed in this research is the in-flight generation of trajectories for autonomous unmanned aircraft, which requires a method of generating pseudo-optimal trajectories in near-real-time, on-board the aircraft, and without external intervention. The focus of this research is the enhancement of a particular inverse dynamics direct method that is a candidate solution to the problem. This research introduces the following contributions to the method. A quaternion-based inverse dynamics model is introduced that represents all orientations without singularities, permits smooth interpolation of orientations, and generates more accurate controls than the previous Euler-angle model. Algorithmic modifications are introduced that: overcome singularities arising from parameterization and discretization; combine analytic and finite difference expressions to improve the accuracy of controls and constraints; remove roll ill-conditioning when the normal load factor is near zero, and extend the method to handle negative-g orientations. It is also shown in this research that quadratic interpolation improves the accuracy and speed of constraint evaluation. The method is known to lead to a multimodal constrained nonlinear optimization problem. The performance of the method with four nonlinear programming algorithms was investigated: a differential evolution algorithm was found to be capable of over 99% successful convergence, to generate solutions with better optimality than the quasi- Newton and derivative-free algorithms against which it was tested, but to be up to an order of magnitude slower than those algorithms. The effects of the degree and form of polynomial airspeed parameterization on optimization performance were investigated, and results were obtained that quantify the achievable optimality as a function of the parameterization degree. Overall, it was found that the method is a potentially viable method of on-board near- real-time trajectory generation for unmanned aircraft but for this potential to be realized in practice further improvements in computational speed are desirable. Candidate optimization strategies are identified for future research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Machine learning and multi-dimension features based adaptive intrusion detection in ICN

As a new network architecture, Information-Centric Networks (ICN) has great advantages in content distribution and can better meet our needs. But it faced with many threats unavoidably. There are four types of attack in ICN: naming related attacks, routing related attacks, caching related attacks and miscellaneous attacks. These attacks will undermine the availability of ICN, the confidentiality and privacy of data. In addition, routers store a large amount of content for the users' request, and it is necessary to protect these intermediate nodes. Since the styles of content stored in nodes are not the same, using a unified set of intrusion detection rules simply will cause a large number of false positives and false negatives. Therefore, every node should perform intrusion detection according to its own characteristics. In this paper, we propose an intrusion detection mechanism to alert for abnormal packets. We introduce a extensive solution using machine learning for attacks in ICN. Moreover, the nodes in this scheme can adapt to the external environment and intelligently detect packets. Simulation on the machine learning algorithm involved prove that the algorithm is effective and suitable for network packets