In model predictive control an optimization problem is solved in every time step, which in real-time applications has to be solved within a limited time frame. When applied on embedded hardware in fast changing systems it is important to use efficient solvers and crucial to guarantee that the optimization problem can be solved within the time frame. In this thesis a path following controller which follows a motion plan given by a motion planner is implemented to steer a truck and trailer system. To solve the optimization problems which in this thesis are quadratic programs the three different solvers DAQP, qpOASES and OSQP are employed. The computational time of the active-set solvers DAQP, qpOASES and the operator splitting solver OSQP are compared, where the controller using DAQP was found the fastest and therefore most suited to use in this application of real-time model predictive control.  A certification framework for the active-set method is used to give complexity guarantees on the controller using DAQP. The exact worst-case number of iterations when the truck and trailer system is following a straight path is presented. Furthermore, initial experiments show that given enough computational time/power the exact iteration complexity can be determined for every possible quadratic program that can appear in the controller.

Autonomous systems are slowly moving into the mainstream with things like self driving cars and autonomous robots in storage facilities already in use today. The aim of this project is to simulate a virtual bakery with a baker-robot (agent)that is able to complete recipes within strict deadlines.Signal temporal logic (STL) is used to define instructions that can be understood by the agent. In order to carry out these instructions, a control barrier function (CBF) is used.CBFs are time and state dependent, are used to describe the desired behavior of the agent, and are designer made. If the CBF corresponding to the task is non-negative from beginning to end during the task, the task has been completed successfully.A virtual robot was used in this project and was tasked with moving to and staying in different areas, which represents picking up and dropping off ingredients, all whilst staying within the boundaries of the bakery. The focus of this work is on completing the large amount (10+) of sequential tasks required to completea recipe. The CBF remained positive during the task, and the task was completed successfully. / Autonoma system börjar ta mer och mer plats i vardagen med saker som självkörande bilar och autonoma robotar i lagerlokaler som redan används idag. Syftet med det här projektet är att simulera ett virtuellt bageri med en bagarrobot (agent) som kan laga recept under strikta tidskrav. Signal temporal logic (STL) används för att definiera instruktioner som kan förstås av agenten. För att genomföra dessa instruktioner korrekt används en control barrier function (CBF). CBF:er är tidsoch tillståndsberoende, används för att beskriva agentens önskade beteende, och är skapade av en designer. Om CBF:en är positiv från början till slut under uppgiftens gång så har uppgiften genomförts som önskat. En virtuell robot användes i det här projektet och fick i uppdrag att flytta till och stanna inom olika områden, vilket representerar att plocka upp och lämna ingredienser, allt medan den vistas inom bageriets gränser. Fokus för detta arbete ligger på att slutföra den stora mängd (10+) av sekventiella uppgifter sim krävs för att laga ett recept. CBF:en var positiv under hela uppgiften, och uppgiften genomfördes framgångsrikt. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

This thesis develops a two dimensional, axisymmetric finite element package for solving continuum problems including rubbleization subsidence and nonlinear fracture mechanics. This package includes both a user friendly preprocessor, PRESCRUBS.BYU, and a versatile analysis code SCRUBS.BYU. PRESCRUBS.BYU systematically creates the data file necessary to run SCRUBS.BYU. SCRUBS.BYU provides many options of nonlinear static analysis using either linear or quadratic isoparametric finite elements. Sample problems are presented that demonstrate the capabilities of this package.

A dimensionally compatible system of equations for stand basal area, volume, and basal area and volume growth was derived using dimensional analysis. These equations are analytically and numerically consistent with dimensionally compatible individual tree volume and taper equations and share parameters with them. Parameters for the system can be estimated by fitting individual tree taper and volume equations or by fitting stand level basal area and volume equations. In either case the parameters are nearly identical. Therefore, parameters for the system can be estimated at the tree or stand level without changing the results. Data from a thinning study in loblolly pine (Pinus taeda L.) plantations established on cutover site-prepared lands were used to estimate the parameters. However, the developed system of equations is general and can be applied to other tree species in other locales. / Ph. D.

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Though multirotor unmanned aerial vehicles (UAVs) have become widely used during the past decade, challenges in autonomy have prevented their widespread use when moving vehicles act as their base stations. Emerging use cases, including maritime surveillance, package delivery and convoy support, require UAVs to autonomously operate in this scenario. This thesis presents improved solutions to both the state estimation and control problems that must be solved to enable robust, autonomous landing of multirotor UAVs onto moving vehicles.Current state-of-the-art UAV landing systems depend on the detection of visual fiducial markers placed on the landing target vehicle. However, in challenging conditions, such as poor lighting, occlusion, or extreme motion, these fiducial markers may be undected for significant periods of time. This thesis demonstrates a state estimation algorithm that tracks and estimates the locations of unknown visual features on the target vehicle. Experimental results show that this method significantly improves the estimation of the state of the target vehicle while the fiducial marker is not detected.This thesis also describes an improved control scheme that enables a multirotor UAV to accurately track a time-dependent trajectory. Rooted in Lie theory, this controller computes the optimal control signal based on an error-state formulation of the UAV dynamics. Simulation and hardware experiments of this control scheme show its accuracy and computational efficiency, making it a viable solution for use in a robust landing system.

Many important tasks in finance often rely on complex and time-consuming computations. The rapid development of quantum technology has raised the question of whether quantum computing can be used to solve these tasks more efficiently than classical computing. This thesis studies the potential use of quantum computing in finance by solving differently-sized problem instances of the mean-variance portfolio selection model using commercially available quantum resources. The experiments employ gate-based quantum computers and quantum annealing, the two main technologies for realizing a quantum computer. To solve the mean-variance optimization problem on gate-based quantum computers, the model was formulated as a quadratic unconstrained binary optimization (QUBO) problem, which was then used as input to quantum resources available on the largest quantum computing as a service (QCaaS) platforms, IBM Quantum Lab, Microsoft Azure Quantum and Amazon Braket. To solve the problem using quantum annealing, a hybrid quantum-classical solver available on the service D-Wave Leap was employed, which takes as input the mean-variance model’s constrained quadratic form. The problem instances were also solved classically on the model’s QUBO form, where the results acted as benchmarks for the performances of the quantum resources. The results were evaluated based on three performance metrics: time-to-solve, solution quality, and cost-to-solve. The findings indicate that gate-based quantum computers are not yet mature enough to consistently find optimal solutions, with the computation times being long and costly as well. Moreover, the use of gate-based quantum computers was not trouble-free, with the majority of quantum computers failing to even complete the jobs. Quantum annealing, on the other hand, demonstrated greater maturity, with the hybrid solver being capable of fast and accurate optimization, even for very large problem instances. The results from using the hybrid solver justify further research into quantum annealing, to better understand the capabilities and limitations of the technology. The results also indicate that quantum annealing has reached a level of maturity where it has the potential to make a significant impact on financial institutions, creating value that cannot be obtained by using classical computing.

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