The goal of this bachelor thesis has been to evaluate and test the available open source software and commercial hardwarefor potential later use as the electrical system in the ALPHAUAV. ALPHA is a student project, with the goal of building an autonomous drone capable of high altitude, long-endurance missions to gather data from electromagnetic phenomena in the atmosphere. Data later to be used in research at the facility ofSpace and Plasma physics at KTH. The evaluation has been done by constructing of an MVP, to prove that the open source softwareand commercial hardware can be used to build an autonomousUAV. / Målet med denna kandidatuppsats har varit att evaluera och testa öppen källkod tillsammans med kommersiell hårdvara för att potentiellt kunna nyttjas som elektriskt system i ALPHA UAV. ALPHA UAV är ett studentprojekt, med målet att bygga en autonom drönare kapabel att genomföra höghöjdsflygningar med lång uthållighet för att kunna samla in data från elektromagnetiska fenomen i atmosfären. Data som senare kan nyttjas i forskningssyfte på institutionen för rymd-och plasmafysik på KTH. Evalueringen har gjorts genom att konstruera en MVP, för att bevsia att öppen källkod och kommersiell hårdvara kan nyttjas för att bygga en autonom UAV. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Unmanned Aerial Systems (UAS) are aircraft without a human pilot on board. They are comprised of a ground-based autonomous or human operated control system, an unmanned aerial vehicle (UAV) and a communication, command and control (C3) link between the two systems. UAS are widely used in military warfare, wildfire mapping, aerial photography, etc primarily to collect and process large amounts of data. While they are highly efficient in data collection and processing, they are susceptible to software espionage and data manipulation. This research aims to provide a novel solution to enhance the security of the flight controller thereby contributing to a secure and robust UAS. The proposed solution begins by introducing a new technology in the domain of flight controllers and how it can be leveraged to overcome the limitations of current flight controllers. The idea is to decouple the applications running on the flight controller from the task of data validation. The authenticity of all external data processed by the flight controller can be checked without any additional overheads on the flight controller, allowing it to focus on more important tasks. To achieve this, we introduce an adjacent controller whose sole purpose is to verify the integrity of the sensor data. The controller is designed using minimal resources from the reconfigurable logic of an FPGA. The secondary I/O processor is implemented on an incipient Zynq SoC based flight controller. The soft-core microprocessor running on the configurable logic of the FPGA serves as a first level check on the sensor data coming into the flight controller thereby forming a trusted boundary layer. / Master of Science / UAV is an aerial vehicle which does not carry a human operator, uses aerodynamic forces to lift the vehicle and is controlled either autonomously by an onboard computer or remotely controlled by a pilot on ground. The software application running on the onboard computer is known as flight controller. It is responsible for guidance and trajectory tracking capabilities of the aircraft. A UAV consists of various sensors to measure parameters such as orientation, acceleration, air speed, altitude, etc. A sensor is a device that detects or measures a physical property. The flight controller continuously monitors the sensor values to guide the UAV along a specific trajectory. Successful maneuvering of a UAV depends entirely on the data from sensors, thus making it vulnerable to sensor data attacks using fabricated physical stimuli. These kind of attacks can trigger an undesired response or mask the occurrence of actual events. In this thesis, we propose a novel approach where we perform a first-level check on the incoming sensor data using a dedicated low cost hardware designed to protect data integrity. The data is then forwarded to the flight controller for further access and processing.

An adaptive autopilot design for an uninhabited surface vehicle Andy SK Annamalai The work described herein concerns the development of an innovative approach to the design of autopilot for uninhabited surface vehicles. In order to fulfil the requirements of autonomous missions, uninhabited surface vehicles must be able to operate with a minimum of external intervention. Existing strategies are limited by their dependence on a fixed model of the vessel. Thus, any change in plant dynamics has a non-trivial, deleterious effect on performance. This thesis presents an approach based on an adaptive model predictive control that is capable of retaining full functionality even in the face of sudden changes in dynamics. In the first part of this work recent developments in the field of uninhabited surface vehicles and trends in marine control are discussed. Historical developments and different strategies for model predictive control as applicable to surface vehicles are also explored. This thesis also presents innovative work done to improve the hardware on existing Springer uninhabited surface vehicle to serve as an effective test and research platform. Advanced controllers such as a model predictive controller are reliant on the accuracy of the model to accomplish the missions successfully. Hence, different techniques to obtain the model of Springer are investigated. Data obtained from experiments at Roadford Reservoir, United Kingdom are utilised to derive a generalised model of Springer by employing an innovative hybrid modelling technique that incorporates the different forward speeds and variable payload on-board the vehicle. Waypoint line of sight guidance provides the reference trajectory essential to complete missions successfully. The performances of traditional autopilots such as proportional integral and derivative controllers when applied to Springer are analysed. Autopilots based on modern controllers such as linear quadratic Gaussian and its innovative variants are integrated with the navigation and guidance systems on-board Springer. The modified linear quadratic Gaussian is obtained by combining various state estimators based on the Interval Kalman filter and the weighted Interval Kalman filter. Change in system dynamics is a challenge faced by uninhabited surface vehicles that result in erroneous autopilot behaviour. To overcome this challenge different adaptive algorithms are analysed and an innovative, adaptive autopilot based on model predictive control is designed. The acronym ‘aMPC’ is coined to refer to adaptive model predictive control that is obtained by combining the advances made to weighted least squares during this research and is used in conjunction with model predictive control. Successful experimentation is undertaken to validate the performance and autonomous mission capabilities of the adaptive autopilot despite change in system dynamics.

In this thesis, control and guidance algorithms for unmanned sea surface vehicles are studied. To design control algorithms of different complexity, first a mathematical model for an unmanned sea surface vehicle is derived. The dynamical and kinematical equations for a sea surface vehicle are obtained, and they are adapted to real life conditions with necessary additions and simplifications. The forces and torques effecting on the vehicle are investigated in detail. Control algorithms for under-actuated six degrees-of-freedom model are designed. PID and LQR controllers are implemented to attain desired surge speed and yaw position. The autopilots are designed and their performances are compared. Based on the autopilots, a guidance algorithm is implemented to achieve desired motions of the vehicle. An obstacle avoidance algorithm is proposed for safe motion among the obstacles. A next-point generation algorithm is designed to direct the vehicle to the most appropriate next way-point if the one ahead is missed. The effects of disturbances on the motion of the vehicle are studied thoroughly on simulation results. PID controller for an unmanned sea surface vehicle is implemented on ArduPilot Mega v1.4 cart controlling a Traxxas Spartan model boat. The performance of the controller is validated. Simulations and experimental results are provided.

Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2011. / ENGLISH ABSTRACT: The development of an integrated avionics system containing all the required sensors and actuators for autopilot control is presented. The thesis analyzes the requirements for the system and presents detailed hardware design. The architecture of the system is based on an FPGA which is tasked with interfacing with the sensors and actuators. The FPGA abstracts a microprocessor from these interface modules, allowing it to focus only on the control and user interface algorithms. Firmware design for the FPGA, as well as a conceptualization of the microprocessor software design is presented. Simulation results showing the functionality of firmware modules are presented. / AFRIKAANSE OPSOMMING: Die ontwikkeling van ‘n geïntegreede avionika‐stelsel wat al die vereiste sensors en aktueerders vir outoloods‐beheer bevat, word voorgestel. Die tesis analiseer die vereistes van die stelsel en stel ‘n hardeware‐ontwerp voor. Die argitektuur van die stelsel bevat ‘n FPGA wat ‘n koppelvlak met sensors en aktueerders skep. Die FPGA verwyder die mikroverwerker weg van hierdie koppelvlak modules en stel dit sodoende in staat om slegs op die beheer en gebruikerskoppelvlak‐algoritmes te fokus. Sagteware‐ontwerp vir die FPGA, asook die konseptualisering van die sagtewareontwerp vir die mikroverwerker, word aangebied. Simulasie resultate wat die funksionaliteit van die FPGA‐sagteware modules aandui, word ook voorgestel.

abstract: With recent advances in missile and hypersonic vehicle technologies, the need for being able to accurately simulate missile-target engagements has never been greater. Within this research, we examine a fully integrated missile-target engagement environment. A MATLAB based application is developed with 3D animation capabilities to study missile-target engagement and visualize them. The high fidelity environment is used to validate miss distance analysis with the results presented in relevant GNC textbooks and to examine how the kill zone varies with critical engagement parameters; e.g. initial engagement altitude, missile Mach, and missile maximum acceleration. A ray-based binary search algorithm is used to estimate the kill zone region; i.e. the set of initial target starting conditions such that it will be "killed". The results show what is expected. The kill zone increases with larger initial missile Mach and maximum acceleration & decreases with higher engagement altitude and higher target Mach. The environment is based on (1) a 6DOF bank-to-turn (BTT) missile, (2) a full aerodynamic-stability derivative look up tables ranging over Mach number, angle of attack and sideslip angle (3) a standard atmosphere model, (4) actuator dynamics for each of the four cruciform fins, (5) seeker dynamics, (6) a nonlinear autopilot, (7) a guidance system with three guidance algorithms (i.e. PNG, optimal, differential game theory), (8) a 3DOF target model with three maneuverability models (i.e. constant speed, Shelton Turn & Climb, Riggs-Vergaz Turn & Dive). Each of the subsystems are described within the research. The environment contains linearization, model analysis and control design features. A gain scheduled nonlinear BTT missile autopilot is presented here. Autopilot got sluggish as missile altitude increased and got aggressive as missile mach increased. In short, the environment is shown to be a very powerful tool for conducting missile-target engagement research - a research that could address multiple missiles and advanced targets. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2016

La stabilisation et l’automatisation du vol de tout véhicule aérien nécessite la mise en oeuvre d’algorithmes de commande. La synthèse et la simulation des lois de commande reposent sur un modèle mathématique du véhicule, qui doit être de complexité et de précision appropriées. Cette thèse présente une méthodologie complète d’identification appliquée à un hélicoptère coaxialminiature. L’étude théorique de son comportement en vol permet d’établir plusieurs modèles basés sur la mécanique du vol, qui diffèrent par les phénomènes aérodynamiques pris en compte. Ils sont identifiés, comparés et validés grâce à des données de vol, mettant en évidence l’importance de certains phénomènes dans la précision du modèle. Différentes lois de commande sont alors étudiées et évaluées en simulation puis par des expérimentations sur un prototype. Les résultats obtenus sont conformes aux simulations numériques, validant ainsi l’ensemble de la démarche. / Control algorithms are at the heart of the stability and automatic flight capabilities of any aerial vehicle. Synthesis and simulation of control laws are based on a mathematicalmodel of the vehicle, which must be a trade-off between simplicity and accuracy. This work presents a complete system identification methodology applied on a miniature coaxial helicopter. Based on flight mechanics and aerodynamics, several models are built. They differ in the aerodynamic phenomena taken into account. They are identified, compared and validated thanks to flight data, highlighting important phenomena in the accuracy of the model. Several flight control strategies are then studied and evaluated through simulations and experiments with a prototype. The results are in accordance with numerical simulations, thus validating the whole approach.

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<div>Autonomous transportation is a research field that has gained huge interest in recent years, with autonomous electric or hydrogen cars coming ever closer to seeing everyday use. Not just cars are subject to autonomous research though, the field of aviation is also being explored for fully autonomous flight. One very important aspect for making autonomous flight a reality is attitude control, the control of roll, pitch, and sometimes yaw. Traditional approaches for automated attitude control use PID (proportional-integral-derivative) controllers, which use hand-tuned parameters to fulfill the task. In this work, however, the use of Reinforcement Learning algorithms for attitude control will be explored. With the surge of more and more powerful artificial neural networks, which have proven to be universally usable function approximators, Deep Reinforcement Learning also becomes an intriguing option. </div><div>A software toolkit will be developed and used to allow for the use of multiple flight simulators to train agents with Reinforcement Learning as well as Deep Reinforcement Learning. Experiments will be run using different hyperparamters, algorithms, state representations, and reward functions to explore possible options for autonomous attitude control using Reinforcement Learning.</div>

This thesis presents the design of an autopilot capable of flying small unmanned aerial vehicles with wingspans less then 21 inches. The autopilot is extremely small and lightweight allowing it to fit in aircraft of this size. The autopilot features an advanced, highly autonomous flight control system with auto-launch and auto-landing algorithms. These features allow the autopilot to be operated by a wide spectrum of skilled and unskilled users. Innovative control techniques implemented in software, coupled with light weight, robust, and inexpensive hardware components were used in the design of the autopilot.