This dissertation explores the realm of high-accuracy localization through the utilization of ranging-based techniques, encompassing a spectrum of signals ranging from low-frequency ultrasound acoustic signals to more intricate high-frequency signals like Wireless Fidelity (Wi-Fi) IEEE 802.11az, 5G New Radio (NR), and 6G. Moreover, another contribution is the conception of a novel timing mechanism and synchronization protocol grounded in tunable quantum photonic oscillators. In general, our primary focus is to facilitate precise indoor localization, where conventional GPS signals are notably absent. To showcase the significance of this innovation, we present two vital use cases at the forefront: drone localization and metaverse user positioning. In the context of indoor drone localization, the spectrum of applications ranges from recreational enthusiasts to critical missions requiring pinpoint accuracy. At the hobbyist level, drones can autonomously navigate intricate indoor courses, enriching the recreational experience. As a finer illustration of a hobbyist application, consider the case of ``follow me drones". These specialized drones are tailored for indoor photography and videography, demanding an exceptionally accurate autonomous flight capability. This precision is essential to ensure the drone can consistently track and capture its designated subject, even as it moves within the confined indoor environment. Moving on from hobby use cases, the technology extends its profound impact to more crucial scenarios, such as search and rescue operations within confined spaces. The ability of drones to localize with high precision enhances their autonomy, allowing them to maneuver seamlessly, even in environments where human intervention proves challenging. Furthermore, the technology holds the potential to revolutionize the metaverse. Within the metaverse, where augmented and virtual realities converge, the importance of high-accuracy localization is amplified. Immersive experiences like Augmented/Virtual/Mixed Reality (AR/VR/MR) gaming rely heavily on precise user positioning to create seamless interactions between digital and physical environments. In entertainment, this innovation sparks innovation in narrative design, enhancing user engagement by aligning virtual elements with real-world surroundings. Beyond entertainment, applications extend to areas like telemedicine, enabling remote medical procedures with virtual guidance that matches physical reality. In light of all these examples, the imperative for an advanced high-accuracy localization system has become increasingly pronounced. The core objective of this dissertation is to address this pressing need by engineering systems endowed with exceptional precision in localization. Among the array of potential techniques suitable for GPS-absent scenarios, we have elected to focus on ranging-based methods. Specifically, our methodologies are built upon the fundamental principles of time of arrival, time difference of arrival, and time of flight measurements. In essence, each of our devised systems harnesses the capabilities of beacons such as ultrasound acoustic sensors, 5G femtocells, or Wi-Fi access points, which function as the pivotal positioning nodes. Through the application of trilateration techniques, based on the calculated distances between these positioning nodes and the integrated sensors on the drone or metaverse user side, we facilitate robust three-dimensional localization. This strategic approach empowers us to realize our ambition of creating localization systems that not only compensate for the absence of GPS signals but also deliver unparalleled accuracy and reliability in complex and dynamic indoor environments. A significant challenge that we confronted during our research pertained to the disparity in z-axis localization performance compared to that of the x-y plane. This nuanced yet pivotal concern often remains overlooked in much of the prevailing state-of-the-art literature, which predominantly emphasizes two-dimensional localization methodologies. Given the demanding context of our work, where drones and metaverse users navigate dynamically across all three dimensions, the imperative for three-dimensional localization became evident. To address this, we embarked on a comprehensive analysis, encompassing mathematical derivations of error bounds for our proposed localization systems. Our investigations unveiled that localization errors trace their origins to two distinct sources: errors induced by ranging-based factors and errors stemming from geometric considerations. The former category is chiefly influenced by factors encompassing the quality of measurement devices, channel quality in which the signal communication between the sensor on the user and the positioning nodes takes place, environmental noise, multipath interference, and more. In contrast, the latter category, involving geometry-induced errors, arises primarily from the spatial configuration of the positioning nodes relative to the user. Throughout our journey, we dedicated efforts to mitigate both sources of error, ensuring the robustness of our system against diverse error origins. Our approach entails a two-fold strategy for each proposed localization system. Firstly, we introduce innovative techniques such as Frequency-Hopping Spread Spectrum (FHSS) and Frequency-Hopping Code Division Multiple Access (FH-CDMA) and incorporate devices such as Reconfigurable Intelligent Surfaces (RIS) and photonic oscillators to fortify the system against errors stemming from ranging-related factors. Secondly, we devised novel evolutionary-based optimization algorithms, adept at addressing the complex NP-Hard challenge of optimal positioning node placement. This strategic placement mitigates the impact of geometry-induced errors on localization accuracy across the entire environmental space. By meticulously addressing both these sources of error, our localization systems stand as a testament to comprehensive robustness and accuracy. Our methodologies not only extend the frontiers of three-dimensional localization but also equip the systems to navigate the intricacies of indoor environments with precision and reliability, effectively fulfilling the evolving demands of drone navigation and metaverse user interaction. / Doctor of Philosophy / In this dissertation, we first explore some promising substitutes for the Global Positioning System (GPS) for the autonomous navigation of drones and metaverse user positioning in indoor spaces. Then, we will make the scope of research more comprehensive and try to explore substitutes to GPS for autonomous navigation of drones in general, both in indoor environments and outdoors. For the first part, we make our small indoor GPS. Similar to GPS, in our system, a receiver onboard the drone or the metaverse user can receive signals from our small semi-satellites in the room, and with that, it can localize itself. The idea is very similar to how the well-known GPS works, with some modifications. Unlike the GPS, we are using acoustic ultrasound signals or some RF signal based on 5G or Wi-Fi for transmission. Also, we have more freedom compared to GPS because, in GPS, they have to transmit signals from far ahead distances, whereas, in our scenario, it is just a room in which we put all of our semi-satellite transmitters. Moreover, we can put them anywhere we want in the room. This is, in fact important, because the positions of these semi-satellites have a huge effect on the accuracy of our system. Also, we can decide how many of them we need to cover every point in the room and not have any blind spots. We propose our novel techniques for finding the optimal placement to improve localization accuracy. In GPS, they propose a technique that is suitable for the case of those satellites and their distance to the targets. Similarly, we offer our novel techniques to have a robust transmission against noise and other factors and guarantee a localization scheme with high accuracy. All being said, our proposed system for indoor localization of drones and metaverse users in three dimensions has considered all the possible sources of error and proposed solutions to conquer them; hence a robust system with high accuracy in three-dimensional space.

Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses. / Master of Science / Unfavorable environmental and (or) human displacement may engender the need for Search and Rescue (SAR). Challenges such as inaccessibility, large search areas, and heavy reliance on available responder count, limited equipment and training makes SAR a challenging problem. Additionally, SAR operations also pose significant risk to involved responders. This opens a remarkable opportunity for robotic systems to assist and augment human understanding of the harsh environments. A large body of work exists on the introduction of ground and aerial robots in visual and temporal inspection of search areas with varying levels of autonomy. Unfortunately, limited autonomy is the norm in such systems, due to the limitations presented by on-board UAV resources and networking capabilities. In this work we propose a new multi-agent approach to SAR and introduce a wearable compute cluster in the form factor of a backpack. The backpack allows offloading compute intensive tasks such as Lost Person Behavior Modelling, Path Planning and Deep Neural Network based computer vision applications away from the UAVs and offers significantly high performance computers to execute them. The backpack also provides for a strong networking backbone and task orchestrators which allow for enhanced coordination and resource sharing among all the agents in the system. On the basis of our benchmarking experiments, we observe that the backpack can significantly boost capabilities and success in modern SAR responses.

One important parameter of interest in remote sensing to date is yield variability. Proper understanding of yield variability provides insight on the geo-positional dependences of field yields and insight on zone management strategies. Estimating cotton yield and observing cotton yield variability has proven to be a challenging problem due to the complex fruiting behavior of cotton from reactions to environmental conditions. Current methods require expensive sensory equipment on large manned aircrafts and satellites. Other systems, such as cotton yield monitors, are often subject to error due to the collection of dust/trash on photo sensors. This study was aimed towards the development of a miniature unmanned aerial system that utilized a first-person view (FPV) color camera for measuring cotton yield variability. Outcomes of the study led to the development of a method for estimating cotton yield variability from images of experimental cotton plot field taken at harvest time in 2014. These plots were treated with nitrogen fertilizer at five different rates to insure variations in cotton yield across the field. The cotton yield estimates were based on the cotton unit coverage (CUC) observed as the cotton boll image signal density. The cotton boll signals were extracted via their diffusion potential in the image intensity space. This was robust to gradients in illumination caused by cloud coverage as well as fruiting positions in the field. These estimates were provided at a much higher spatial resolution (9.0 cm2) at comparable correlations (R2=0.74) with current expensive systems. This method could prove useful for the development of low cost automated systems for cotton yield estimation as well as yield estimation systems for other crops. / Master of Science

In this dissertation, two main topics are explored, the vehicle routing problem (VRP) and model reference adaptive control (MRAC) for unknown nonlinear systems. The VRP and its extension, the split delivery VRP (SVRP), are analyzed to determine the effects of using two different objective functions, the total cost objective, and the last delivery objective. A worst-case analysis suggests that using the SVRP can improve total costs by as much as a factor of 2 and the last delivery by a factor that scales with the number of vehicles over the classical VRP. To test the theoretical worst-cases against the solutions of benchmark datasets, a heuristic is developed based on embedding a random variable neighborhood search within an iterative local search heuristic. Results suggest that the split deliveries do in fact improve total cost and last delivery times over the classical formulation. The SVRP has been developed classically for use with vehicles such as trucks which have large payload capacities and typically long ranges for deliveries, but are limited to traversing on roads. Unmanned aerial vehicles (UAVs) are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. The classical SVRP formulation is extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of Euclidean distances, path plans which are adjusted for a known, constant wind underlie the cost matrix of the optimization problem. The effects of payload on the vehicle's range are developed using propeller momentum theory, and simulations verify that the proposed approach could be used in a realistic scenario. Two novel MRAC laws are then developed. The first, MRAC laws for prescribed performance, exploits barrier Lyapunov functions and a 2-Layer approach to guarantee user-defined performance. This control law allows unknown nonlinear systems to verify a user-defined rate of convergence of the tracking error while verifying apriori control and tracking error constraints. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. The second novel MRAC law is MRAC for switched dynamical systems which is proven in two different mathematical frameworks. Applying the Caratheodory framework, it is proven that if the switching signal has an arbitrarily small, but non-zero, dwell-time, then solutions of both the trajectory tracking error's and the adaptive gains' dynamics exist, are unique, and are defined almost everywhere, and the trajectory tracking error converges asymptotically to zero. Employing the Filippov framework, it is proven that if the switching signal is Lebesgue integrable and has countably many points of discontinuity, then maximal solutions of both the trajectory tracking error and the adaptive gains dynamics exist and are defined almost everywhere, and the trajectory tracking error converges to zero asymptotically. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. The previous results are then combined into a novel application in construction. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements. / Doctor of Philosophy / The main goal of this dissertation is to provide an implementable approach to the routing and control problem for unmanned aerial vehicles (UAVs) tasked with delivering payloads or taking images or videos of known locations. To plan routes for the fleet of vehicles, a split vehicle routing (SVRP) approach is utilized. UAVs are useful for their high maneuverability, but suffer from limited capacity for payloads and short ranges. Before extending the SVRP to a formulation more suitable for UAVs, we study the effects of using two different objective functions on the solutions to the optimization problem through a worst-case analysis. Namely, we study the minimum total cost function and the minimum last delivery function and their effects on both the classical vehicle routing problem (VRP), where only one vehicle can visit each customer, and the SVRP, where multiple vehicles can visit each customer. A custom heuristic is developed to solve several benchmark instances, and the results suggest that using the SVRP can save in total cost and last delivery over the VRP when using the same objective functions. The classical SVRP formulation is then extended to one more suitable for UAVs by accounting for limited range, limited payloads, and the ability to swap batteries at known locations. Instead of using straight line approaches to traversing between locations, a path planning approach is utilized and wind is accounted for. The effects of payload on the vehicle's range are also considered, and simulations verify that the proposed approach could be used in a realistic scenario. After developing a routing approach for UAVs, the control problem is considered. The first control approach developed is for unknown nonlinear systems which necessitate control and tracking error constraints that can be set before the start of the mission. This result is achieved using a novel model reference adaptive control (MRAC) approach. In addition to verifying the constraints, a drawback of classical MRAC approaches, the poor performance in the transient stages, is addressed by providing the ability to guarantee a user-defined rate of convergence of the system. Numerical simulations are performed on the roll dynamics of a delta-wing aircraft. A second MRAC approach is then developed for the cases in which the UAVs may be tasked with installing a payload at the customer location. An approach is used where the vehicles are considered to have different flight states, one where the vehicle is in free flight, and one where the vehicle contacts the wall. These types of systems are denoted as switched dynamical systems, and an adaptive control law is developed for unknown nonlinear switched plants that must follow the trajectory of user-defined linear switched reference models. The proposed MRAC law is experimentally verified in the case where a UAV with tilting propellers is tasked with mounting an unknown camera onto a wall. Finally, we seek to combine the new routing and control approach into an application to improve safety within a construction site. A method for using a UAV to measure autonomously the moisture of an exterior precast concrete envelope is developed which can provide data feedback through contact-based measurements to improve safety and real-time data acquisition through the integration with the Building Information Model (BIM). To plan the path of the vehicle, the path planning and SVRP for UAV approaches developed in previous chapters are utilized. To enable the UAS to contact surfaces, a switched MRAC law is employed to control the vehicle throughout and guarantee successful measurements. A full physics-based simulation environment is developed, and the proposed framework is used to simulate taking multiple measurements.

Cyber-physical systems (CPSs) are large-scale systems that seamlessly integrate physical and human elements via a cyber layer that enables connectivity, sensing, and data processing. Key examples of CPSs include smart power systems, smart transportation systems, and the Internet of Things (IoT). This wide-scale cyber-physical interconnection introduces various operational benefits and promises to transform cities, infrastructure, and networked systems into more efficient, interactive, and interconnected smart systems. However, this ubiquitous connectivity leaves CPSs vulnerable to menacing security threats as evidenced by the recent discovery of the Stuxnet worm and the Mirai malware, as well as the latest reported security breaches in a number of CPS application domains such as the power grid and the IoT. Addressing these culminating security challenges requires a holistic analysis of CPS security which necessitates: 1) Determining the effects of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, 2) Analyzing the multi-agent interactions -- among humans and automated systems -- that occur within CPSs and which have direct effects on the security state of the system, and 3) Recognizing the role that humans and their decision making processes play in the security of CPSs. Based on these three tenets, the central goal of this dissertation is to enhance the security of CPSs with human actors by developing fool-proof defense strategies founded on novel theoretical frameworks which integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models. Towards realizing this overarching goal, this dissertation presents a number of key contributions targeting two prominent CPS application domains: the smart electric grid and drone systems. In smart grids, first, a novel analytical framework is developed which generalizes the analysis of a wide set of security attacks targeting the state estimator of the power grid, including observability and data injection attacks. This framework provides a unified basis for solving a broad set of known smart grid security problems. Indeed, the developed tools allow a precise characterization of optimal observability and data injection attack strategies which can target the grid as well as the derivation of optimal defense strategies to thwart these attacks. For instance, the results show that the proposed framework provides an effective and tractable approach for the identification of the sparsest stealthy attacks as well as the minimum sets of measurements to defend for protecting the system. Second, a novel game-theoretic framework is developed to derive optimal defense strategies to thwart stealthy data injection attacks on the smart grid, launched by multiple adversaries, while accounting for the limited resources of the adversaries and the system operator. The analytical results show the existence of a diminishing effect of aggregated multiple attacks which can be leveraged to successfully secure the system; a novel result which leads to more efficiently and effectively protecting the system. Third, a novel analytical framework is developed to enhance the resilience of the smart grid against blackout-inducing cyber attacks by leveraging distributed storage capacity to meet the grid's critical load during emergency events. In this respect, the results demonstrate that the potential subjectivity of storage units' owners plays a key role in shaping their energy storage and trading strategies. As such, financial incentives must be carefully designed, while accounting for this subjectivity, in order to provide effective incentives for storage owners to commit the needed portions of their storage capacity for possible emergency events. Next, the security of time-critical drone-based CPSs is studied. In this regard, a stochastic network interdiction game is developed which addresses pertinent security problems in two prominent time-critical drone systems: drone delivery and anti-drone systems. Using the developed network interdiction framework, the optimal path selection policies for evading attacks and minimizing mission completion times, as well as the optimal interdiction strategies for effectively intercepting the paths of the drones, are analytically characterized. Using advanced notions from Nobel-prize winning prospect theory, the developed framework characterizes the direct impacts of humans' bounded rationality on their chosen strategies and the achieved mission completion times. For instance, the results show that this bounded rationality can lead to mission completion times that significantly surpass the desired target times. Such deviations from the desired target times can lead to detrimental consequences primarily in drone delivery systems used for the carriage of emergency medical products. Finally, a generic security model for CPSs with human actors is proposed to study the diffusion of threats across the cyber and physical realms. This proposed framework can capture several application domains and allows a precise characterization of optimal defense strategies to protect the critical physical components of the system from threats emanating from the cyber layer. The developed framework accounts for the presence of attackers that can have varying skill levels. The results show that considering such differing skills leads to defense strategies which can better protect the system. In a nutshell, this dissertation presents new theoretical foundations for the security of large-scale CPSs, that tightly integrate cyber, physical, and human elements, thus paving the way towards the wide-scale adoption of CPSs in tomorrow's smart cities and critical infrastructure. / Ph. D. / Enhancing the efficiency, sustainability, and resilience of cities, infrastructure, and industrial systems is contingent on their transformation into more interactive and interconnected smart systems. This has led to the emergence of what is known as cyber-physical systems (CPSs). CPSs are widescale distributed and interconnected systems integrating physical components and humans via a cyber layer that enables sensing, connectivity, and data processing. Some of the most prominent examples of CPSs include the smart electric grid, smart cities, intelligent transportation systems, and the Internet of Things. The seamless interconnectivity between the various elements of a CPS introduces a wealth of operational benefits. However, this wide-scale interconnectivity and ubiquitous integration of cyber technologies render CPSs vulnerable to a range of security threats as manifested by recently reported security breaches in a number of CPS application domains. Addressing these culminating security challenges requires the development and implementation of fool-proof defense strategies grounded in solid theoretical foundations. To this end, the central goal of this dissertation is to enhance the security of CPSs by advancing novel analytical frameworks which tightly integrate the cyber, physical, and human elements of a CPS. The developed frameworks and tools enable the derivation of holistic defense strategies by: a) Characterizing the security interdependence between the various elements of a CPS, b) Quantifying the consequences of possible attacks on a CPS and the effectiveness of any implemented defense mechanism, c) Modeling the multi-agent interactions in CPSs, involving humans and automated systems, which have a direct effect on the security state of the system, and d) Capturing the role that human perceptions and decision making processes play in the security of CPSs. The developed tools and performed analyses integrate the engineering principles of CPSs with the mathematical concepts of game theory and human behavioral models and introduce key contributions to a number of CPS application domains such as the smart electric grid and drone systems. The introduced results enable strengthening the security of CPSs, thereby paving the way for their wide-scale adoption in smart cities and critical infrastructure.

International Telemetering Conference Proceedings / October 18-21, 2004 / Town & Country Resort, San Diego, California / This paper describes a system of Total Border Surveillance, which is cost effective, closes existing gaps and is less manpower intensive than the current techniques. The system utilizes a fleet of commercially available aircraft converted to unmanned capability, existing GPS and surveillance systems and autonomous ground stations to provide the desired coverage.

Thesis (PhD (Electrical and Electronic Engioneering))--Stellenbosch University, 2008. / Unmanned Aerial Vehicle (UAV) technology is classified as being disruptive since it has the potential to radically change the utilization of airspace. Most unmanned vehicles are aimed at military applications, yet civilian applications of unmanned aerial vehicle technology could benefit South Africa considerably. At present, the lack of UAV regulations and standards precludes UAVs from being certified to operate on a file and fly basis in un-segregated civilian airspace. The inability for UAVs to be certified because of a lack of standards creates a “chicken and egg” – “stale mate” situation. If principles such as “equivalence”, initially proposed by Eurocontrol are adopted in South Africa, it then follows that equivalent standards used by manned aircraft could be used by UAVs. UAVs must therefore be tested and evaluated in order to prove compliance with equivalent existing manned aircraft regulations in the foreseeable future until UAV regulations and standards become available. It has been suggested that specific UAV missions such as maritime patrol, border control, search & rescue, and cargo transport could fulfil current requirements. Design considerations and possible concepts of UAV operations, maintenance and training that will enable UAVs to satisfy the immediate South African strategic requirements whilst complying with existing airspace and airworthiness regulations have been proposed in this document while further UAV specific standards and regulations are being developed. UAV testing is an essential part of proving the enabling technology, and part of the process of gaining acceptance into wider airspace. Fortunately, flight test methods and procedures applicable to manned aircraft are directly applicable to UAVs, while systems unique to UAVs can be adapted from existing procedures applied to missiles and military UAVs. Once UAVs are developed and tested, it will be necessary to start full scale operations. Some considerations will be necessary during mission planning. Air traffic management regulations however will prohibit some UAVs from operating in all airspace until enabling technology is developed and tested, while some existing UAVs will never be permitted to “file and fly”. This study also analyses existing airspace and UAV platforms in order to identify the airspace and platforms that will have the most chance of being successfully permitted to “file and fly” in civil airspace. For South Africa to advance as a UAV operating and manufacturing nation, it is therefore essential to compile a roadmap that will guide the process of developing, certifying and operating UAVs. The roadmap must include an interim process, as well as stating the end objective, which is “file and fly”. This South African UAV Roadmap proposal is based on international research that uses documentation and lessons learned from elsewhere to guide the process for creating UAV regulations and standards, while allowing existing UAV operations to expand into the existing airspace in order for further UAV research to take place. This roadmap proposal is the conclusion of a 3 year study, and references to the applicable literature are made throughout the document.

Thesis (MScEng)--Stellenbosch University, 2011. / ENGLISH ABSTRACT: This thesis presents the development of an adaptive longitudinal control system for an unmanned aerial vehicle (UAV). The project forms part of a research effort at Stellenbosch University into different fault-tolerant control techniques for UAVs. In order to demonstrate the usefulness of fault-tolerant adaptive control, the control system was designed to handle damage-induced longitudinal shifts in the centre of gravity (CG) of the aircraft, which are known to have a dramatic effect on the stability of a fixed-wing aircraft. Using a simplified force and moment model, equations were derived which model the effect of longitudinal CG shifts on the behaviour of the aircraft. A linear analysis of the longitudinal dynamics using these equations showed that the short period mode can become unstable for backward CG shifts. An adaptive pitch rate controller with the model reference adaptive control structure was designed to re-stabilise the short period mode when the CG shifts backwards. The adaptive law was designed using Lyapunov stability theory. Airspeed, climb rate and altitude controllers were designed around the pitch rate controller to allow full autonomous control of the longitudinal dynamics of the UAV. These outer loops were designed with constant parameters, since they would be unaffected by CG shifts if the adaptive pitch rate controller performed as desired. Pure software simulations as well as hardware-in-the-loop simulations showed that the adaptive control system is able to handle instantaneous shifts in the centre of gravity which would destabilise a fixed-gain control system. These simulation results were validated in flight tests, where the aircraft was destabilised using positive feedback and re-stabilised by the adaptive control system. Thus the simulation and flight test results showed that an adaptive control can re-stabilise an unstable aircraft without explicit knowledge of the change in the aircraft dynamics, and therefore could be effective as part of an integrated fault-tolerant control system. / AFRIKAANSE OPSOMMING: Hierdie tesis bied die ontwikkeling aan van ’n aanpassende longitudinale beheerstelsel vir ’n onbemande vliegtuig. Die projek is deel van navorsing by die Universiteit van Stellenbosch oor verskillende fout-tolerante beheertegnieke vir onbemande vliegtuie. Om die doeltreffendheid van aanpassende beheer te demonstreer, is die beheerstelsel ontwerp om situasies te kan hanteer waar die vliegtuig só beskadig word dat sy massamiddelpunt agtertoe skuif, wat ’n groot invloed op die stabiliteit van ’n vastevlerk-vliegtuig kan hê. ’n Vereenvoudigde model van die kragte en momente wat op die vliegtuig inwerk is gebruik om vergelykings af te lei wat beskryf hoe die gedrag van die vliegtuig verander as die massamiddelpunt agtertoe verskuif. Hierdie vergelykings is gebruik in ’n lineêre analise van die longitudinale dinamika van die vliegtuig, wat getoon het dat die kortperiode-modus onstabiel kan raak as die massamiddelpunt agtertoe verskuif. ’n Aanpassende heitempobeheerder met die modelverwysings-aanpassende beheerstruktuur is ontwerp om die kortperiode-modus weer te stabiliseer wanneer die massamiddelpunt agtertoe verskuif. Die aanpassingswet is ontwerp deur die gebruik van Lyapunov se stabiliteitsteorie. Lugspoed-, klimtempo- en hoogtebeheerders is rondom die aanpassende heitempobeheerder ontwerp sodat die longitudinale dinamika van die vliegtuig heeltemal outonoom beheer kan word. Hierdie buitelusse is ontwerp met vaste parameters, aangesien hulle nie geraak sal word deur verskuiwings in die massamiddelpunt as die aanpassende heitempobeheerder na wense werk nie. Suiwer sagteware-simulasies, sowel as hardeware-in-die-lus-simulasies, het getoon dat die aanpassende beheerstelsel oombliklike verskuiwings in die massamiddelpunt goed kan hanteer, waar sulke verskuiwings ’n beheerstelsel met vaste parameters onstabiel sou maak. Hierdie simulasie-resultate is bevestig deur vlugtoetse te doen, waar die vliegtuig onstabiel gemaak is deur positiewe terugvoer, en weer deur die aanpassende beheerstelsel stabiel gemaak is. Die simulasie- en vlugtoetsresultate wys dus dat aanpassende beheer ’n onstabiele vliegtuig weer kan stabiliseer sonder eksplisiete kennis van die veranderinge in die dinamika van die vliegtuig. Aanpassende beheer kan dus doeltreffend wees as deel van ’n geïntegreerde fout-tolerante beheerstelsel.

Veículos Aéreos não Tripulados (VANTs) são aeronaves que voam sem tripulação e são capazes de realizar diversos tipos de missões, como vigilância, coleta de dados topográficos e monitoramento ambiental. Este é um domínio que tem muito a ganhar com a aplicação da abordagem de Linha de Produtos de Software (LPS), uma vez que é rico em variabilidades e cada modelo de VANT tem também muitas partes comuns. Neste trabalho é apresentada uma infraestrutura tecnológica e de configuração de ativos em Simulink, gerenciados pelas ferramentas Pure::variant e Hephaestos para uma LPS de VANTs. Um conjunto de padrões para especificação de variabilidades em Simulink é proposto, bem como uma extensão para a ferramenta Hephaestus. Uma comparação entre as ferramentas Pure::variants e Hephaestus é apresentada / Unmanned Aerial Vehicles (UAVs) are aircrafts that can fly without any crew and are capable to realize several types of missions such as surveillance, topographic data collection and environmental monitoring. This is a domain which can benefit very much with the adoption of the Software Product Lines (SPL) approach, as each UAV model is rich in variabilities and has many common parts. In this work it is presented a software asset configuration infrastructure for the Simulink environment, managed by the tools Pure::variants and Hephaestus for a UAV SPL. A set of patterns of variability specification in Simulink is proposed as well as an extension to Hephaestus to support a SPL product engineering for Simulink. A comparison between Pure::variants and Hephaestus is also presented

Este trabalho dedica-se ao projeto e desenvolvimento de um veículo aéreo não tripulado. Tais veículos podem ser utilizados em diversas aplicações como monitoramento, vigilância, transporte, resgate, entre outros. Dentre os diversos tipos de veículos aéreos, este trabalho irá focar no modelo do quadrirotor, composto por quatro hélices contra-rotoras que estabilizam e movimentam o veículo. Para alcançar o objetivo de controlar este tipo de veículo, várias propostas e metodologias podem ser aplicadas, todas buscando contemplar o controle de todas ou parte das variáveis de estado presentes nesta planta. Neste texto serão descritas: as equações cinemáticas e dinâmicas que regem este sistema; o projeto e composição mecânica da aeronave; definição de sensores e atuadores juntamente com seus métodos de utilização; implementação de controlador linear por alocação direta de polos e Regulador Linear Quadrático juntamente com observador de estados de ordem plena e filtro de Kalman, para recuperação de estados não mensurados e filtragem de ruídos. Serão apresentados resultados em simulações para cada método de controle selecionado visando optar pelo melhor controlador para a aplicação da aeronave. O método selecionado será implementado para controlar a aeronave com os sensores e atuadores selecionados. Esta implementação será realizada a partir da técnica HIL Hardware in The Loop juntamente com o software MATLAB/Simulink visando validar o controlador em conjunto com a planta real, bem como o modelo dinâmico construído. / This work is dedicated to the project and development of an unmanned aerial vehicle. Such vehicles can be employed in various applications such as monitoring, surveillance, transportation, rescue and others. Among the types of aerial vehicles, this work is focused on the quadrotor, composed by four counter-rotating propellers which stabilize and displace the vehicle. In order to fulfill the objective of controlling this vehicle, many methodologies and propositions can be applied, seeking the control of all or a snippet of the state variables present in the system. There will be described in this work: the cinematic and dynamic equations that govern this system; the mechanical project and construction of the aircraft; sensors and actuators definition, along with its usage methods; linear control implementation of the pole placement and Linear Quadratic Regulator techniques along full order state observer and Kalman filtering in order to recover and filter non-measured states. Performance results in simulations will be presented on each control implementation to validate the best controller for the application and this implementation will be applied on the projected aircraft using the sensors and actuators selected. This implementation will be given through the HIL - Hardware in the Loop method using MATLAB/Simulink software to validate the control technique applied and the constructed dynamic model.