Este trabalho apresenta maneiras de trabalhar com o controle de potência reativa e estabilidade de tensão em microgrids. A estratégia de controle utilizada é o Controle por Tensão Distribuída (Distributed Voltage Control - DVC), ou controle por tensões distribuídas, um laço integral que considera as potências reativas em todas as barras como entradas e as tensões respectivas como sinais de controle. Diferentes estratégias de controle para distribuição de potência foram propostas e analisadas, sempre enfatizando seus aspectos conceituais. O cálculo dos ganhos do controlador, embora fundamental para o sucesso de qualquer estratégia de controle, geralmente não é discutido, e não são dados métodos ou linhas gerais para esta tarefa. Neste trabalho, apresentamos e discutimos diferentes metodologias para o projeto de ganhos de controle em DVC. Além disso, sendo o sistema não-linear, grandes variações de performance podem ser observadas se os mesmos ganhos de controle são usados para todos os pontos de operação, o que motiva a proposta de uma estratégia de programação de ganhos, também apresentada neste trabalho. / This text deals with the control of reactive power distribution and voltage stability in microgrids. The control strategy studied is the Distributed Voltage Control (DVC), an integral loop considering entries as reactive in every bus and the bus voltages as control signals. Different control strategies for power distribution have been proposed and analysed, always emphasising its conceptual aspects; design of the controller’s gains, however fundamental for the success of any control strategy, is usually not discussed, and no methods or guideline are given for this task. In this text we present and discuss different methodologies for tuning the control gains in DVC. Moreover, since power systems are nonlinear, large variations in performance can be observed if the same control gains are used for all operating points, which motivates the proposal of a gain scheduling strategy, also presented in here.

Orientador: Gilberto Pechoto de Melo / Banca: Luiz de Paula do Nascimento / Banca: Silmara Cassola / Resumo: O presente trabalho apresenta a metodologia dos observadores de estado para a detecção e localização de falhas em sistemas contendo elementos finitos de placas de Kirchoff. Tal metodologia consiste na montagem de um banco de observadores de estado, o qual é capaz de detectar falhas presentes no sistema, além de localizar o componente danificado e a porcentagem de falha. As matrizes de ganho dos observadores de estado foram determinadas por dois métodos distintos: o método Regulador Quadrático Linear e das Desigualdades Matriciais Lineares. Nesse trabalho, foi utilizada uma placa plana fina montada sobre um sistema de suspensão similar ao de uma plataforma veicular, representando um veiculo simplificadamente. A modelagem da plataforma utilizada foi realizada mediante a utilização do método dos elementos finitos, considerando-se diferentes números de elemento no modelo. O tipo do elemento finito utilizado foi o elemento de placa de Kirchoff. Adicionalmente foi analisada a influência de elementos de controle junto à suspensão da plataforma no movimento da mesma. O modelo considerado, juntamente com os programas computacionais desenvolvidos, foram utilizados para a simulação do movimento da plataforma. Na literatura, normalmente são apresentadas simulações para o movimento de apenas ¼ do veículo, neste trabalho, no entanto, os programas desenvolvidos podem simular o movimento do veiculo inteiro. Foram realizadas simulações computacionais para o movimento da plataforma com a finalidade de se detectar e localizar falhas introduzidas nos elementos da suspensão e também foram realizados testes experimentais, com os mesmos fins. Mediante tais testes (teóricos e experimentais) verificou-se a eficácia da metodologia desenvolvida e a sua principal limitação: o número de elementos finitos considerado no modelo relacionado com o número de medidas efetuadas e a observabilidade do sistema. / Abstract: This work presents the state observers methodology for the detection and location of faults in systems containing finite elements of plate of Kirchoff. This methodology consists in the assembly of a bank of state observers, which is capable of detecting faults in the system, and also to locate the damaged component and the percentage of failure. The gain matrices of the state observers were determined by two different methods, these are the method Linear Quadratic Regulator and the Linear Matrix Inequalities. In this work was considered a thin plate mounted on a suspension system that is similar to a vehicle platform, representing a vehicle in a simplified way. The modeling of the platform used was performed by using the finite element method, considering different numbers of element in the model. The finite element used was the Kirchoff's plate element. It was also studied the influence of elements of control, together with the suspension of the platform, into its movement. The model considered, together with the developed computational programs, were used to simulate the movement of the platform. In the literature, usually are presented simulations for the movement of only ¼ of the vehicle, in this work, the developed programs can simulate the movement of the entire vehicle. It was realized computational simulations for the movement of the platform in order to detect and locate faults introduced in the elements of the suspension and experimental tests were also conducted with the same purpose. Through such tests (theoretical and experimental) it was verified the effectiveness of the developed methodology and its major limitation: the number of finite elements considered in the model related with the number of outputs and the observability of the system. / Mestre

Este trabalho apresenta maneiras de trabalhar com o controle de potência reativa e estabilidade de tensão em microgrids. A estratégia de controle utilizada é o Controle por Tensão Distribuída (Distributed Voltage Control - DVC), ou controle por tensões distribuídas, um laço integral que considera as potências reativas em todas as barras como entradas e as tensões respectivas como sinais de controle. Diferentes estratégias de controle para distribuição de potência foram propostas e analisadas, sempre enfatizando seus aspectos conceituais. O cálculo dos ganhos do controlador, embora fundamental para o sucesso de qualquer estratégia de controle, geralmente não é discutido, e não são dados métodos ou linhas gerais para esta tarefa. Neste trabalho, apresentamos e discutimos diferentes metodologias para o projeto de ganhos de controle em DVC. Além disso, sendo o sistema não-linear, grandes variações de performance podem ser observadas se os mesmos ganhos de controle são usados para todos os pontos de operação, o que motiva a proposta de uma estratégia de programação de ganhos, também apresentada neste trabalho. / This text deals with the control of reactive power distribution and voltage stability in microgrids. The control strategy studied is the Distributed Voltage Control (DVC), an integral loop considering entries as reactive in every bus and the bus voltages as control signals. Different control strategies for power distribution have been proposed and analysed, always emphasising its conceptual aspects; design of the controller’s gains, however fundamental for the success of any control strategy, is usually not discussed, and no methods or guideline are given for this task. In this text we present and discuss different methodologies for tuning the control gains in DVC. Moreover, since power systems are nonlinear, large variations in performance can be observed if the same control gains are used for all operating points, which motivates the proposal of a gain scheduling strategy, also presented in here.

After introducing concepts from convex analysis, we study how to continuously transform one convex function into another. A natural choice is the arithmetic average, as it is pointwise continuous; however, this choice fails to average functions with different domains. On the contrary, the proximal average is not only continuous (in the epi-topology) but can actually average functions with disjoint domains. In fact, the proximal average not only inherits strict convexity (like the arithmetic average) but also inherits smoothness and differentiability (unlike the arithmetic average). Then we introduce a computational framework for computer-aided convex analysis. Motivated by the proximal average, we notice that the class of piecewise linear-quadratic (PLQ) functions is closed under (positive) scalar multiplication, addition, Fenchel conjugation, and Moreau envelope. As a result, the PLQ framework gives rise to linear-time and linear-space algorithms for convex PLQ functions. We extend this framework to nonconvex PLQ functions and present an explicit convex hull algorithm. Finally, we discuss a method to find primal-dual symmetric antiderivatives from cyclically monotone operators. As these antiderivatives depend on the minimal and maximal Rockafellar functions [5, Theorem 3.5, Corollary 3.10], it turns out that the minimal and maximal function in [12, p.132,p.136] are indeed the same functions. Algorithms used to compute these antiderivatives can be formulated as shortest path problems. / Graduate Studies, College of (Okanagan) / Graduate

Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn't the trailer it trained on.

We discuss a parallel library of efficient algorithms for the solution of linear-quadratic optimal control problems involving largescale systems with state-space dimension up to $O(10^4)$. We survey the numerical algorithms underlying the implementation of the chosen optimal control methods. The approaches considered here are based on invariant and deflating subspace techniques, and avoid the explicit solution of the associated algebraic Riccati equations in case of possible ill-conditioning. Still, our algorithms can also optionally compute the Riccati solution. The major computational task of finding spectral projectors onto the required invariant or deflating subspaces is implemented using iterative schemes for the sign and disk functions. Experimental results report the numerical accuracy and the parallel performance of our approach on a cluster of Intel Itanium-2 processors.

The purpose of this thesis is to investigate different control strategies on a differential drive vehicle. The vehicle should be able to drive in turns at high speed and slowly when it should park next to a charger. In both these cases, good precision in both orientation and distance to the path is important. A PID and an LQ controller have been implemented for this purpose. The two controllers were first implemented in a simulation environment. After implementing the controllers on the system itself, tests to evaluate the controllers were made to imitate real-life situations. This includes tests regarding driving with different speeds in different turns, tests with load distributions, and tests with stopping accuracy. The existing controller on the system was also tested and compared to the new controllers. After evaluating the controllers, it was stated that the existing controller was the most robust. It was not affected much by the load distribution compared to the new controllers. However, the LQ controller was slightly better in most cases, even though it was highly affected by the load distribution. The PID controller performed best regarding stopping accuracy but was the least robust controller by the three. Since the existing controller has a similar performance as the LQ controller but is more robust, the existing controller was chosen as the best one.

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.

This thesis investigates and implements the sensor fusion necessary to autonomously reverse a full size truck and trailer system. This is done using a LiDAR mounted on the rear of the truck along with a RTK-GPS. It is shown that the relative angles between truck-dolly and dolly-trailer can be estimated, along with global position and global heading of the trailer. This is then implemented in one of Scania's test vehicles, giving it the ability to continuously estimate these states. A controller is then implemented, showing that the full scale system can be stabilised in reverse motion. The controller is tested both on a static reference path and a reference path received from a motion planner. In these tests, the controller is able to stabilise the system well, allowing the truck to do complex manoeuvres backwards. A small lateral tracking error is present, which needs to be further investigated.

O estudo da estabilidade e desempenho em sistemas de controle é um tópico relevante na teoria de sistemas. Quando são assumidas incertezas no modelo da planta, existe uma maior dificuldade para garantir um nível de desempenho adequado do sistema dinâmico e a estabilidade pode ser comprometida. Neste trabalho são utilizados um regulador linear quadrático robusto e um filtro de Kalman robusto combinados em uma única formulação para tratar de sistemas dinâmicos incertos em tempo real. O caso de estudo selecionado é o pêndulo invertido. Seus principais desafios de controle encontrados na literatura: estabilização, seguimento e levantamento-captura, serão considerados. Os algoritmos utilizados são motivados pelo fato de que problemas estocásticos podem ser resolvidos por meio de argumentos determinísticos, baseados nos conceitos de função penalidade e mínimos quadrados regularizados. Desta forma, é possível a obtenção do melhor desempenho em contrapartida à máxima influência de incerteza admissível. A análise de desempenho do controlador robusto é realizada por meio de ensaios práticos incluindo incertezas na planta, ruído nos sensores e distúrbios no sinal de controle do pêndulo. / The study of stability and performance in control systems is a relevant topic in systems theory. When uncertainties are considered in the model of the plant, there is a greater difficulty in ensuring an appropriate performance level of the dynamic system, plus, the stability could be compromised as well. In this dissertation a robust linear quadratic regulator and a robust Kalman filter are used in a unified manner to deal with uncertain dynamic systems in real time. The selected case study is the inverted pendulum. Its main control challenges found in the literature will be considered: stabilization, tracking and catching swing-up. The used algorithms are motivated by the fact that stochastic problems can be solved through deterministic arguments based on the concepts of penalty function and regularized least-squares. Thus, it is possible to obtain an optimal performance for the maximum acceptable uncertainty. The performance analysis of the robust control is carried out by practical experiments including uncertainties in the plant, noise in the sensors and disturbance in the pendulum control signal.