In this thesis the problem of autonomous landing of an unmanned aerial vehicle named AE-2 is addressed. The guidance and control technique is developed and demonstrated through numerical simulation results. The complete work includes Mathematical modeling, Control design, Guidance and State estimation for AE-2, which is a fixed wing vehicle with 2m wing span and 6kg weight. The aerodynamic data for AE-2 is available from static wind tunnel tests. Functional fit is done on the wind tunnel data with least squares method to find static aerodynamic coefficients. The aerodynamic forces and moment coefficients are highly nonlinear some of them are partitioned in two zones based on the angle of attack. The dynamic derivatives are found with Athena Vortex Lattice software. For the validation of vortex lattice method the static derivatives obtained by the wind tunnel tests and vortex lattice method, are compared before finding dynamic derivatives. The dynamics of the servo actuators for the aerodynamic control surfaces is incorporated in the simulation. The nonlinear dynamic inversion technique has been used for the guidance and control design. The control is structured in two loops, outer and inner loop. The goal of outer loop is to track the guidance commands of altitude, roll angle and yaw angle by converting them into body rate commands through dynamic inversion. The inner loop than tracks these commanded roll rate, pitch rate and yaw rate by finding the required deflection of control surfaces. The forward velocity of the vehicle is controlled by varying the throttle. A controller for actuator is also designed to reduce the lag. The guidance for landing consists of three phases approach, glideslope and flare. During approach the vehicle is aligned with the runway and guided to a specified height from where the glideslope can begin. The glideslope is straight line path specified by a flight path angle which is restricted between 3 to 4 degree. At the end of glideslope which is marked by flare altitude the flare maneuver begins which is an exponential curve. The problem of transition between the glideslope and flare has addressed by ensuring continuity and smoothness at transition. The exponential curve of flare is designed to end below the ground so that it intersects the ground at a prespecified point. The sink rate at touchdown is also controlled along with the location of touchdown point. The state estimation has been done with Extended Kalman Filter in continuous discrete formulation. The external disturbances like wind shear and wind gust are accounted by appending them in state variables. Further the control design with guidance is tested from various initial conditions, in presence of wind disturbances. The designed filter has also been tested for parameter uncertainty.

Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed Electric powered Unmanned Aerial Vehicles (UAVs) have emerged as a promi- nent aviation concept due to the advantageous such as stealth operation and zero emission. In addition, fuel cell powered electric UAVs are more attrac- tive as a result of the long endurance capability of the propulsion system. This dissertation investigates novel power management architecture for fuel cell and battery powered unmanned aerial vehicle propulsion application. The research work focused on the development of a power management system to control the hybrid electric propulsion system whilst optimizing the fuel cell air supplying system performances. The multiple power sources hybridization is a control challenge associated with the power management decisions and their implementation in the power electronic interface. In most applications, the propulsion power distribu- tion is controlled by using the regulated power converting devices such as unidirectional and bidirectional converters. The amount of power shared with the each power source is depended on the power and energy capacities of the device. In this research, a power management system is developed for polymer exchange membrane fuel cell and Lithium-Ion battery based hybrid electric propulsion system for an UAV propulsion application. Ini- tially, the UAV propulsion power requirements during the take-off, climb, endurance, cruising and maximum velocity are determined. A power man- agement algorithm is developed based on the UAV propulsion power re- quirement and the battery power capacity. Three power states are intro- duced in the power management system called Start-up power state, High power state and Charging power state. The each power state consists of the power management sequences to distribute the load power between the battery and the fuel cell system. A power electronic interface is developed with a unidirectional converter and a bidirectional converter to integrate the fuel cell system and the battery into the propulsion motor drive. The main objective of the power management system is to obtain the controlled fuel cell current profile as a performance variable. The relationship between the fuel cell current and the fuel cell air supplying system compressor power is investigated and a referenced model is developed to obtain the optimum compressor power as a function of the fuel cell current. An adaptive controller is introduced to optimize the fuel cell air supplying system performances based on the referenced model. The adaptive neuro-fuzzy inference system based controller dynamically adapts the actual compressor operating power into the optimum value defined in the reference model. The online learning and training capabilities of the adaptive controller identify the nonlinear variations of the fuel cell current and generate a control signal for the compressor motor voltage to optimize the fuel cell air supplying system performances. The hybrid electric power system and the power management system were developed in real time environment and practical tests were conducted to validate the simulation results.

Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded.

This work outlines a design for a small form-factor flight control system designed to fly in a wide variety of airframes. The system was designed with future expansion in mind while providing a complete, all-in-one solution to meet present needs. This system as presented meets most needs while remaining relatively low cost. It has a completely integrated IMU solution as well as on- board GPS. It is capable of basic waypoint navigation. This solution was testing using software and hardware-in-the-loop simulation which proved its functionality.

Next-generation wireless cellular networks are morphing into a massive Internet of Things (IoT) environment that integrates a heterogeneous mix of wireless-enabled devices such as unmanned aerial vehicles (UAVs) and connected vehicles. This unprecedented transformation will not only drive an exponential growth in wireless traffic, but it will also lead to the emergence of new wireless service applications that substantially differ from conventional multimedia services. To realize the fifth generation (5G) mobile networks vision, a new wireless radio technology paradigm shift is required in order to meet the quality of service requirements of these new emerging use cases. In this respect, one of the major components of 5G is self-organized networks. In essence, future cellular networks will have to rely on an autonomous and self-organized behavior in order to manage the large scale of wireless-enabled devices. Such an autonomous capability can be realized by integrating fundamental notions of artificial intelligence (AI) across various network devices. In this regard, the main objective of this thesis is to propose novel self-organizing and AI-inspired algorithms for optimizing the available radio resources in next-generation wireless cellular networks. First, heterogeneous networks that encompass licensed and unlicensed spectrum are studied. In this context, a deep reinforcement learning (RL) framework based on long short-term memory cells is introduced. The proposed scheme aims at proactively allocating the licensed assisted access LTE (LTE-LAA) radio resources over the unlicensed spectrum while ensuring an efficient coexistence with WiFi. The proposed deep learning algorithm is shown to reach a mixed-strategy Nash equilibrium, when it converges. Simulation results using real data traces show that the proposed scheme can yield up to 28% and 11% gains over a conventional reactive approach and a proportional fair coexistence mechanism, respectively. In terms of priority fairness, results show that an efficient utilization of the unlicensed spectrum is guaranteed when both technologies, LTE-LAA and WiFi, are given equal weighted priorities for transmission on the unlicensed spectrum. Furthermore, an optimization formulation for LTE-LAA holistic traffic balancing across the licensed and the unlicensed bands is proposed. A closed form solution for the aforementioned optimization problem is derived. An attractive aspect of the derived solution is that it can be applied online by each LTE-LAA small base station (SBS), adapting its transmission behavior in each of the bands, and without explicit communication with WiFi nodes. Simulation results show that the proposed traffic balancing scheme provides a better tradeoff between maximizing the total network throughput and achieving fairness among all network ows compared to alternative approaches from the literature. Second, UAV-enabled wireless networks are investigated. In particular, the problems of interference management for cellular-connected UAVs and the use of UAVs for providing backhaul connectivity to SBSs are studied. Speci cally, a deep RL framework based on echo state network cells is proposed for optimizing the trajectories of multiple cellular-connected UAVs while minimizing the interference level caused on the ground network. The proposed algorithm is shown to reach a subgame perfect Nash equilibrium upon convergence. Moreover, an upper and lower bound for the altitude of the UAVs is derived thus reducing the computational complexity of the proposed algorithm. Simulation results show that the proposed path planning scheme allows each UAV to achieve a tradeoff between minimizing energy efficiency, wireless latency, and the interference level caused on the ground network along its path. Moreover, in the context of UAV-enabled wireless networks, a UAV-based on-demand aerial backhaul network is proposed. For this framework, a network formation algorithm, which is guaranteed to reach a pairwise stable network upon convergence, is presented. Simulation results show that the proposed scheme achieves substantial performance gains in terms of both rate and delay reaching, respectively, up to 3.8 and 4-fold increase compared to the formation of direct communication links with the gateway node. Overall, the results of the different proposed schemes show that these schemes yield significant improvements in the total network performance as compared to current existing literature. In essence, the proposed algorithms can also provide self-organizing solutions for several resource management problems in the context of new emerging use cases in 5G networks, such as connected autonomous vehicles and virtual reality headsets.

Philosophiae Doctor - PhD / Unmanned Aerial Vehicles (UAVs) have recently o ered signi cant technological achievements. The advancement in related applications predicts an extended need for automated data muling by UAVs, to explore high risk places, ensure e ciency and reduce the cost of various products and services. Due to advances in technology, the actual UAVs are not as expensive as they once were. On the other hand, they are limited in their ight time especially if they have to use fuel. As a result, it has recently been proposed that they could be assisted by the ground static sensors which provide information of their surroundings. Then, the UAVs need only to provide actions depending on information received from the ground sensors. In addition, UAVs need to cooperate among themselves and work together with organised ground sensors to achieve an optimal coverage. The system to handle the cooperation of UAVs, together with the ground sensors, is still an interesting research topic which would bene t both rural and urban areas. In this thesis, an e cient ground sensor network for optimal UAVs coverage is rst proposed. This is done using a clustering scheme wherein, each cluster member transmits its sensor readings to its cluster head. A more e cient routing scheme for delivering readings to cluster head(s) for collection by UAVs is also proposed. Furthermore, airborne sensor deployment models are provided for e cient data collection from a unique sensor/target. The model proposed for this consists of a scheduling technique which manages the visitation of UAVs to target. Lastly, issues relating to the interplay between both types of sensor (airborne and ground/underground) networks are addressed by proposing the optimal UAVs task allocation models; which take caters for both the ground networking and aerial deployment. Existing network and tra c engineering techniques were adopted in order to handle the internetworking of the ground sensors. UAVs deployment is addressed by adopting Operational Research techniques including dynamic assignment and scheduling models. The proposed models were validated by simulations, experiments and in some cases, formal methods used to formalise and prove the correctness of key properties.

Aerial photography obtained by UAVs (Unmanned Aerial Vehicles) is an emerging market for civil applications. Small UAVs are believed to close gaps in niche markets, such as acquiring airborne image data for remote sensing purposes. Small UAVs will be able to fly at low altitudes, in dangerous environments and over long periods of time. However, the small lightweight constructions of these UAVs lead to new problems, such as higher agility leading to more susceptibility to turbulence and limitations in space and payload for sensor systems. This research investigates the use of low-cost fisheye lenses to overcome such problems which theoretically makes the airborne imaging less sensitive to turbulence. The fisheye lens has the benet of a large observation area (large field of view) and doesn't add additional weight to the aircraft, like traditional mechanical stabilizing systems. This research presents the implementation of a fisheye lens for aerial photography and mapping purposes, including theoretical background of fisheye lenses. Based on the unique feature of the distortion being a function of the viewing angle, methods used to derive the fisheye lens distortion are presented. The lens distortion is used to rectify the fisheye images before these images can be used in aerial photography. A detailed investigation into the inner orientation of the camera and inertial sensor is given, as well as the registration of airborne collected images. It was found that the attitude estimation is critical towards accurate mapping using low quality sensors. A loosely coupled EKF filter applied to the GPS and inertial sensor data estimated the attitude to an accuracy of 3-5° (1-sigma) using low-cost sensors typically found in small UAVs. However, the use of image stitching techniques may improve the outcome. On the other hand, lens distortion caused by the fisheye lens can be addressed by rectification techniques and removed to a sub-pixel level. Results of the process present image sequences gathered from a piloted aircraft demonstrating the achieved performance and potential applications towards UAVs. Further, an unforeseen issue with a vibrating part in the lens lead to the need for vibration compensation. The vibration could be estimated to ±1 pixel in 75% of the cases by applying an extended Hough transform to the fisheye images.

Thesis (M.S.) -- Worcester Polytechnic Institute. / Keywords: Genetic algorithms; planform; optimization; micro aerial vehicle. Includes bibliographical references (p.71-73).

Thesis (M.S. in Aeronautical Engineering)--Naval Postgraduate School, June 1990. / Thesis Advisor(s): Howard, Richard M. Second Reader: Lindsey, Gerald H. "June 1990." Description based on signature page as viewed on October 21, 2009. DTIC Identifier(s): Composite materials, ducted fan, airframes, vertical takeoff aircraft, remotely piloted vehicles. Author(s) subject terms: UAV, composites, AROD, TDF, RPV, ducted fan, vertical takeoff. Includes bibliographical references (p. 74-75). Also available online.

Operation of Unmanned Air Vehicles (UAVs) has increased significantly over the past few years. However, routine operation in non-segregated airspace remains a challenge, primarily due to nature of the environment and restrictions and challenges that accompany this. Currently, tight human control is envisaged as a means to achieve the oft quoted requirements of transparency , equivalence and safety. However, the problems of high cost of human operation, potential communication losses and operator remoteness remain as obstacles. One means of overcoming these obstacles is to devolve authority, from the ground controller to an on-board system able to understand its situation and make appropriate decisions when authorised. Such an on-board system is known as an Autonomous System. The nature of the autonomous system, how it should be designed, when and how authority should be transferred and in what context can they be allowed to control the vehicle are the general motivation for this study. To do this, the system must overcome the negative aspects of differentiators that exist between UASs and manned aircraft and introduce methods to achieve required increases in the levels of versatility, cost, safety and performance. The general thesis of this work is that the role and responsibility of an airborne autonomous system are sufficiently different from those of other conventionally controlled manned and unmanned systems to require a different architectural approach. Such a different architecture will also have additional requirements placed upon it in order to demonstrate acceptable levels of Transparency, Equivalence and Safety. The architecture for the system is developed from an analysis of the basic requirements and adapted from a consideration of other, suitable candidates for effective control of the vehicle under devolved authority. The best practices for airborne systems in general are identified and amalgamated with established principles and approaches of robotics and intelligent agents. From this, a decision architecture, capable of interacting with external human agencies such as the UAS Commander and Air Traffic Controllers, is proposed in detail. This architecture has been implemented and a number of further lessons can be drawn from this. In order to understand in detail the system safety requirements, an analysis of manned and unmanned aircraft accidents is made. Particular interest is given to the type of control moding of current unmanned aircraft in order to make a comparison, and prediction, with accidents likely to be caused by autonomously controlled vehicles. The effect of pilot remoteness on the accident rate is studied and a new classification of this remoteness is identified as a major contributor to accidents A preliminary Bayesian model for unmanned aircraft accidents is developed and results and predictions are made as an output of this model. From the accident analysis and modelling, strategies to improve UAS safety are identified. Detailed implementations within these strategies are analysed and a proposal for more advanced Human-Machine Interaction made. In particular, detailed analysis is given on exemplar scenarios that a UAS may encounter. These are: Sense and Avoid , Mission Management Failure, Take Off/Landing, and Lost Link procedures and Communications Failure. These analyses identify the nature of autonomous, as opposed to automatic, operation and clearly show the benefits to safety of autonomous air vehicle operation, with an identifiable decision architecture, and its relationship with the human controller. From the strategies and detailed analysis of the exemplar scenarios, proposals are made for the improvement of unmanned vehicle safety The incorporation of these proposals into the suggested decision architecture are accompanied by analysis of the levels of benefit that may be expected. These suggest that a level approaching that of conventional manned aircraft is achievable using currently available technologies but with substantial architectural design methodologies than currently fielded. / ©Cranfield University © BAE Systems