Approved for public release, distribution is unlimited / A control system was developed for autonomous visual tracking of a stationary target using a small unmanned aerial vehicle. The kinematic equations of this problem were developed, and the insight obtained from examination was applied in developing controllers for the system. This control system controlled the orientation of the camera to keep it constantly pointing at the target, and also controlled the trajectory of the aircraft in flight around the target. The initial control law that was developed drives the aircraft trajectory to a constant radius around the target. The range to the target is not directly measurable, so it was estimated using steady state Kalman filters. Once a range estimate is obtained, it is used to control the range to the target, and the aircraft trajectory is driven toward a circle with a specified radius. Initial tests of the control system with Simulink simulations have shown good performance of the control system. Further testing with hardware will be conducted, and flight tests are scheduled to be conducted in the near future. Conclusions are drawn and recommendations for further study are presented. / Ensign, United States Navy

M.Ing. / Linear control system theory is well developed and has lead to a number of control system types with well-defined design methods that can be applied to any linear system. Unfortunately, no system in nature is truly linear. As a result, such non-linear systems must be represented by a linear model that is accurate over some region of the operating states of the system. The success of linear control theory in commercial applications is testament to the fact that some types of systems can be adequately represented by a linear model. However, systems with time-varying dynamics or non-linearities such as input or operating state saturation cannot always be adequately controlled by linear control systems. For that reason, non-linear control techniques must be investigated. This project aims to investigate Non-linear Model Predictive Control theory and practical implementation in the context of developing an autopilot for an Unmanned Aerial Vehicle based on a miniature helicopter. A non-linear model of the dynamics of an X-Cell Spectra G radio-controlled helicopter was developed based on the existing literature. A number of experiments were performed to determine the parameters of this model. Significant future work exists in designing additional ground experiments since certain parameters are difficult to measure safely in the laboratory. Additional work to improve the accuracy of the model at high airspeeds, as well as incorporating a more accurate yaw dynamics model, is also required. Following this, a Non-linear Model Predictive Control autopilot was simulated using MATLAB®. The simulation tested the effects of control system parameters such as control horizon and sampling period, as well as the sensor noise susceptibility and its ability to handle wind as a random disturbance. The results determined adequate control system parameters for level flight as well as landing the helicopter under ideal conditions. Simulations in which sensor noise and wind were added showed that the control system is significantly affected by sensor noise and that it cannot hover in the presence of wind. A real-time implementation was not achieved during this work; however, several directions for future research have been discussed.

Ce travail étudie certains des problèmes les plus pertinents dans le sens de la navigation et contrôle présentés dans une classe particulière de mini-véhicules aériens. L'un des principaux objectifs c'est à réaliser un véhicule léger et facile à déployer dans un court laps de temps, un véhicule sans pilote drone capable de suivre une mission complète, du décollage aux points de cheminement suivants et de terminer la mission avec un atterrissage autonome à l'intérieur d'une zone délimitée en utilisant une interface graphique dans un ordinateur ou une tablette. La génération de trajectoire II est la partie qui dit le drone où il doit voyager et sont générés par un algorithme intégré sur le drone. Le résultat classique de Dubins est utilisé comme base pour la génération de trajectoire en 2D et nous avons étendu à la génération de trajectoire 3D. Une stratégie de suivi de trajectoire développée en utilisant l'approche de Lyapunov, est présentée pour piloter un drone à voilure fixe à travers tout le chemin désiré. Le concept clé derrière le contrôleur de suivi de trajectoire s'appuie sur la réduction de la distance entre le centre de masse de l'avion p et le point sur la trajectoire q à zéro, ainsi que l'angle entre le vecteur vitesse et la tangente à la trajectoire. Afin de tester les techniques mises au point au cours de la thèse une application C# -Net personnalisée a été développé nommé MAV3DSim (Multi-Aerial Vehicle 3D Simulator). Le MAV3DSim permet une opération de lecture/écriture de/vers le moteur de simulation à partir de laquelle nous pourrions recevoir toutes les informations de capteurs émulés et envoyés par le simulateur. Le système complet est capable d'effectuer un décollage et d'atterrissage autonome, à travers des points de suivi. Ceci est accompli en utilisant chacune des stratégies développées au cours de la thèse. Nous avons une stratégie pour le décollage et l'atterrissage, ce qui est généré par la partie de navigation qui est le générateur de trajectoire. Une fois que nous avons généré le chemin, il est utilisé par la stratégie de suivi de trajectoire et avec ce que nous avons l'atterrissage et le décollage autonome. / This work studies some of the most relevant problems in the direction of navigation and control presented in a particular class of mini‐aircraft. One of the main objectives is to build a lightweight and easy to deploy vehicle in a short period of time, an unmanned aerial vehicle capable of following a complete mission from take‐o⁄ to the following waypoints and complete the mission with an autonomous landing within a delimitated area using a graphical interface in a computer. The Trajectory Generation It is the part that tells the drone where it must travel and are generated by an algorithm built into the drone. The classic result of Dubins is used as a basis for the trajectory generation in 2D and we have extended it to the 3D trajectory generation. A path following strategy developed using the Lyapunov approach is presented to pilot a fixed wing drone across the desired path. The key concept behind the tracking controller is the reduction of the distance between the center of mass of the aircraft p and the point q on the path to zero, as well as the angle between the velocity vector and the vector tangent to the path. In order to test the techniques developed during the thesis a customized C # .Net application was developed called MAV3DSim (Multi‐Aerial Vehicle 3D Simulator). The MAV3DSim allows a read / write operation from / to the simulation engine from which we could receive all emulated sensor information and sent to the simulator. The MAV3DSim consists of three main elements, the simulation engine, the computation of the control law and the visualization interface. The simulation engine is in charge of the numeric integration of the dynamic equations of the vehicle, we can choose between a quadrotor and a xed wing drone for use in simulation. The visualization interface resembles a ground station type of application, where all variables of the vehicle s state vector can be represented on the same screen. The experimental platform functions as a test bed for the control law prototyping. The platform consists of a xed wing aircraft with a PX4 which has the autopilot function as well as a Raspberry PI mini‐computer which to the implementation of the generation and trajectory tracking. The complete system is capable of performing an autonomous take‐o⁄and landing, through waypoints. This is accomplished by using each of the strategies developed during the thesis. We have a strategy for take‐o⁄ and landing, which is generated by the navigationon part that is the trajectory generator. Once we have generated the path, it is used by the trajectory tracking strategy and withthat we have landing and take‐o⁄ autonomously.

Are drones revolutionary? Reading about military unmanned aerial vehicles (UAVs), or 'drones', one could be led to believe that drones are a revolutionary technology, set to fundamentally change warfare. Their fast proliferation, the association with Science Fiction, combined with the secrecy that surrounds drone use has led many to conclude that the 'Unmanned Revolution' is upon us. This thesis studies the Unmanned Revolution. It develops a framework based on the concept of the 'Revolution in Military Affairs' and applies it to the study of three countries' drone uses and integration into their armed forces. It furthermore explores the role that the designation as revolutionary has played for the integration and use of UAVs in the United States, Germany, and the United Kingdom. It shows that drones have proven their worth in military operations and compares the three countries' experiences. This thesis' detailed assessment of how the different countries have adopted drones and what implication this adoption has had, makes it a work of reference, in particular with regard to the German and British case studies. Assessing five types of changes - operational, doctrinal, strategic, organisational, and social and societal - this thesis argues that the most fundamental, and possibly revolutionary, change caused by military drones is social, namely, the fundamentally changed experience of war by combatants. In addition, it highlights country-specific changes. It concludes that the designation of drones as revolutionary has had an important impact in one country, Germany, although in the opposite way than initially expected. Namely, the intense debate around UAVs has hindered drone procurement and doctrinal thinking. In the other two countries, the Unmanned Revolution narrative was less prevalent and hence less influential. As drones are proliferating globally, I hope my thesis can be of use to policy-makers, military decision-makers as well as researchers worldwide.

A new material system was developed for fabricating the combustion engine of an unmanned aerial vehicle. The material system consisted of a mixture of nanoscale and microscale particles of silicon nitride. Magnesia and yttria were used as sintering additives. The powders were mixed with a paraffin binder system. The binder-powder was analyzed for its properties and molding attributes. The study involved several steps of the development and processing. These steps include torque rheometery analysis, mixing scale-up, property measurements of binder-powder, injection molding, binder removal, sintering, scanning electron microscopy analysis and mechanical properties measurements. Simulations of the injection molding process were conducted to assess the feasibility of manufacturing a ceramic engine and to determine its optimal process parameters. The model building required for the simulation was based on flow and solidification behavior data compiled for the binder-powder mixture. The simulations were performed using the Moldfow software package. A design of experiments approach was set up in order to gain an understanding of critical process parameters as well as identifying a feasible process window. Quality criteria were then analyzed in order to determine the optimal production parameters. The study resulted in the successful development of design parameters that will enable fabrication of silicon nitride engine components by powder injection molding. / Graduation date: 2012

This dissertation investigates the development of a methodology suitable for the evaluation of advanced propulsion concepts. At early stages of development, both the future performance of these concepts and their requirements are highly uncertain, making it difficult to forecast their future value. A systematic methodology to identify potential advanced propulsion concepts and assess their robustness is necessary to reduce the risk of developing advanced propulsion concepts. Existing advanced design methodologies have evaluated the robustness of technologies or concepts to variations in requirements, but they are not suitable to evaluate a large number of dissimilar concepts. Variations in requirements have been shown to impact the development of advanced propulsion concepts, and any method designed to evaluate these concepts must incorporate the possible variations of the requirements into the assessment. In order to do so, a methodology had to do two things. First, it had to systemically identify a probabilistic distribution for the future requirements. Such a distribution would allow decision-makers to quantify the uncertainty introduced by variations in requirements. Second, the methodology must assess the robustness of the propulsion concepts as a function of that distribution. These enabling elements have been synthesized into new methodology, the Evolving Requirements Technology Assessment (ERTA) method. The ERTA method was used to evaluate and compare advanced propulsion systems as possible power systems for a hurricane tracking, High Altitude, Long Endurance (HALE) unmanned aerial vehicle (UAV). The problem served as a good demonstration of the ERTA methodology because conventional propulsion systems will not be sufficient to power the UAV, but the requirements for such a vehicle are still uncertain.

Unmanned aerial vehicles (UAV's) have been the focus of significant research interest in both military and commercial areas since they have a variety of practical applications including reconnaissance, surveillance, target acquisition, search and rescue, patrolling, real-time monitoring, and mapping, to name a few. To increase the autonomy and the capability of these UAV's and thus to reduce the workload of human operators, typical autonomous UAV's are usually equipped with both a navigation system and a tracking system. The navigation system provides high-rate ownship states (typically ownship inertial position, inertial velocity, and attitude) that are directly used in the autopilot system, and the tracking system provides low-rate target tracking states (typically target relative position and velocity with respect to the ownship). Target states in the global frame can be obtained by adding the ownship states and the target tracking states. The data estimated from this combination of the navigation system and the tracking system provide key information for the design of most UAV guidance laws, control command generation, trajectory generation, and path planning. As a baseline system that estimates ownship states, an integrated navigation system is designed by using an extended Kalman filter (EKF) with sequential measurement updates. In order to effectively fuse various sources of aiding sensor information, the sequential measurement update algorithm is introduced in the design of the integrated navigation system with the objective of being implemented in low-cost autonomous UAV's. Since estimated state accuracy using a low-cost, MEMS-based IMU degrades with time, several absolute (low update rate but bounded error in time) sensors, including the GPS receiver, the magnetometer, and the altimeter, can compensate for time-degrading errors. In this work, the sequential measurement update algorithm in smaller vectors and matrices is capable of providing a convenient framework for fusing the many sources of information in the design of integrated navigation systems. In this framework, several aiding sensor measurements with different size and update rates are easily fused with basic high-rate IMU processing. In order to provide a new mechanism that estimates ownship states, a new nonlinear filtering framework, called the unscented Kalman filter (UKF) with sequential measurement updates, is developed and applied to the design of a new integrated navigation system. The UKF is known to be more accurate and convenient to use with a slightly higher computational cost. This filter provides at least second-order accuracy by approximating Gaussian distributions rather than arbitrary nonlinear functions. This is compared to the first-order accuracy of the well-known EKF based on linearization. In addition, the step of computing the often troublesome Jacobian matrices, always required in the design of an integrated navigation system using the EKF, is eliminated. Furthermore, by employing the concept of sequential measurement updates in the UKF, we can add the advantages of sequential measurement update strategy such as easy compensation of sensor latency, easy fusion of multi-sensors, and easy addition and subtraction of new sensors while maintaining those of the standard UKF such as accurate estimation and removal of Jacobian matrices. Simulation results show better performance of the UKF-based navigation system than the EKF-based system since the UKF-based system is more robust to initial accelerometer and rate gyro biases and more accurate in terms of reducing transient peaks and steady-state errors in ownship state estimation. In order to estimate target tracking states or target kinematics, a new vision-based tracking system is designed by using a UKF in the scenario of three-dimensional air-to-air tracking. The tracking system can estimate not only the target tracking states but also several target characteristics including target size and acceleration. By introducing the UKF, the new vision-based tracking system presents good estimation performance by overcoming the highly nonlinear characteristics of the problem with a relatively simplified formulation. Moreover, the computational step of messy Jacobian matrices involved in the target acceleration dynamics and angular measurements is removed. A new particle filtering framework, called an extended marginalized particle filter (EMPF), is developed and applied to the design of a new vision-based tracking system. In this work, only three position components with vision measurements are solved in particle filtering part by applying Rao-Blackwellization or marginalization approach, and the other dynamics, including the target nonlinear acceleration model, with Gaussian noise are effectively handled by using the UKF. Since vision information can be better represented by probabilistic measurements and the EMPF framework can be easily extended to handle this type of measurements, better performance in estimating target tracking states will be achieved by directly incorporating non-Gaussian, probabilistic vision information as the measurement inputs to the vision-based tracking system in the EMPF framework.

Unmanned Aerial Vehicles (UAVs) are the most dynamic growth sector of the aerospace industry today. The need to provide persistent intelligence, surveillance, and reconnaissance for military operations is driving the planned acquisition of over 5,000 UAVs over the next five years. The most pressing need is for quiet, small UAVs with endurance beyond what is capable with advanced batteries or small internal combustion propulsion systems. Fuel cell systems demonstrate high efficiency, high specific energy, low noise, low temperature operation, modularity, and rapid refuelability making them a promising enabler of the small, quiet, and persistent UAVs that military planners are seeking. Despite the perceived benefits, the actual near-term performance of fuel cell powered UAVs is unknown. Until the auto industry began spending billions of dollars in research, fuel cell systems were too heavy for useful flight applications. However, the last decade has seen rapid development with fuel cell gravimetric and volumetric power density nearly doubling every 2-3 years. As a result, a few design studies and demonstrator aircraft have appeared, but overall the design methodology and vehicles are still in their infancy. The design of fuel cell aircraft poses many challenges. Fuel cells differ fundamentally from combustion based propulsion in how they generate power and interact with other aircraft subsystems. As a result, traditional multidisciplinary analysis (MDA) codes are inappropriate. Building new MDAs is difficult since fuel cells are rapidly changing in design, and various competitive architectures exist for balance of plant, hydrogen storage, and all electric aircraft subsystems. In addition, fuel cell design and performance data is closely protected which makes validation difficult and uncertainty significant. Finally, low specific power and high volumes compared to traditional combustion based propulsion result in more highly constrained design spaces that are problematic for design space exploration. To begin addressing the current gaps in fuel cell aircraft development, a methodology has been developed to explore and characterize the near-term performance of fuel cell powered UAVs. The first step of the methodology is the development of a valid MDA. This is accomplished by using propagated uncertainty estimates to guide the decomposition of a MDA into key contributing analyses (CAs) that can be individually refined and validated to increase the overall accuracy of the MDA. To assist in MDA development, a flexible framework for simultaneously solving the CAs is specified. This enables the MDA to be easily adapted to changes in technology and the changes in data that occur throughout a design process. Various CAs that model a polymer electrolyte membrane fuel cell (PEMFC) UAV are developed, validated, and shown to be in agreement with hardware-in-the-loop simulations of a fully developed fuel cell propulsion system. After creating a valid MDA, the final step of the methodology is the synthesis of the MDA with an uncertainty propagation analysis, an optimization routine, and a chance constrained problem formulation. This synthesis allows an efficient calculation of the probabilistic constraint boundaries and Pareto frontiers that will govern the design space and influence design decisions relating to optimization and uncertainty mitigation. A key element of the methodology is uncertainty propagation. The methodology uses Systems Sensitivity Analysis (SSA) to estimate the uncertainty of key performance metrics due to uncertainties in design variables and uncertainties in the accuracy of the CAs. A summary of SSA is provided and key rules for properly decomposing a MDA for use with SSA are provided. Verification of SSA uncertainty estimates via Monte Carlo simulations is provided for both an example problem as well as a detailed MDA of a fuel cell UAV. Implementation of the methodology was performed on a small fuel cell UAV designed to carry a 2.2 kg payload with 24 hours of endurance. Uncertainty distributions for both design variables and the CAs were estimated based on experimental results and were found to dominate the design space. To reduce uncertainty and test the flexibility of the MDA framework, CAs were replaced with either empirical, or semi-empirical relationships during the optimization process. The final design was validated via a hardware-in-the loop simulation. Finally, the fuel cell UAV probabilistic design space was studied. A graphical representation of the design space was generated and the optima due to deterministic and probabilistic constraints were identified. The methodology was used to identify Pareto frontiers of the design space which were shown on contour plots of the design space. Unanticipated discontinuities of the Pareto fronts were observed as different constraints became active providing useful information on which to base design and development decisions.

Currently there is ongoing research in the field of Mobile Ad-hoc Networks (MANET) for several different scenarios. Research has focused on topology related challenges such as routing mechanisms or addressing systems, as well as security issues like traceability of radio communication or encryption. In addition, there are very specific research interests such as the effects of directional antennas for MANETs or optimized transmission techniques for minimal power consumption or range optimization. Unmanned aerial vehicles (UAVs), and unmanned aerial systems (UAS) in general, need wireless systems in order to communicate. Current UAS are very flexible and allow for a wide spectrum of mission profiles by means of utilizing different UAVs, according to the requirements at hand. Each mission poses special needs and requirements on the internal and external UAS communication and special mission scenarios calling for UAV swarms increase the complexity and require specialized communication solutions. UAS have specific needs not provided by the general research, but are, on the other hand, to diversified to make much use of narrowly focused developments; UAS form a sufficiently large research area for application of MANETs to be considered as an independent group with specialized needs worthy of tailored implementations of MANET principles. MANET research has not tackled a general approach to UAS although some sources show specific applications involving UAVS. This work presents some new aspects for the development of of ad-hoc wireless networks for UAVs and UAS and focuses on their specialties and needs. A general framework for MANET development is proposed. Furthermore, the proposed specific evaluation scenarios provide for a UAS focused comparison of MANET performance.

This dissertation describes the development of a requirements analysis methodology that takes into account the concept of operations and the hierarchical decomposition of aerospace systems. At the core of the methodology, the Analytic Network Process (ANP) is used to ensure the traceability between the qualitative and quantitative information present in the hierarchical model. The proposed methodology is implemented to the requirements definition of a hurricane tracker Unmanned Aerial Vehicle. Three research objectives are identified in this work; (1) improve the requirements mapping process by matching the stakeholder expectations with the concept of operations, systems and available resources; (2) reduce the epistemic uncertainty surrounding the requirements and requirements mapping; and (3) improve the requirements down-selection process by taking into account the level of importance of the criteria and the available resources. Several challenges are associated with the identification and definition of requirements. The complexity of the system implies that a large number of requirements are needed to define the systems. These requirements are defined early in the conceptual design, where the level of knowledge is relatively low and the level of uncertainty is large. The proposed methodology intends to increase the level of knowledge and reduce the level of uncertainty by guiding the design team through a structured process. To address these challenges, a new methodology is created to flow-down the requirements from the stakeholder expectations to the systems alternatives. A taxonomy of requirements is created to classify the information gathered during the problem definition. Subsequently, the operational and systems functions and measures of effectiveness are integrated to a hierarchical model to allow the traceability of the information. Monte Carlo methods are used to evaluate the variations of the hierarchical model elements and consequently reduce the epistemic uncertainty. The proposed methodology is applied to the design of a hurricane tracker Unmanned Aerial Vehicles to demonstrate the origin and impact of requirements on the concept of operations and systems alternatives. This research demonstrates that the hierarchical modeling methodology provides a traceable flow-down of the requirements from the problem definition to the systems alternatives phases of conceptual design.