The automotive industry has been involved in making vehicles autonomous to different levels in the past decade rapidly. Particularly in the commercial vehicle market, there is a significant necessity to make trucks have a certain level of automation to help reduce dependence on human efforts to drive. This could help in reducing several accidents caused by human error. Interestingly there are several challenges and solutions in achieving and implementing autonomous driving for trucks. First, a benchmark of different control architectures that can make a truck drive autonomously are explored. The chosen controllers (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control and Model Predictive Control) vary in their simplicity in implementation and versatility in handling different vehicle parameters and constraints. A thorough comparison of these path tracking controllers are performed using several metrics. Second, a collision avoidance system based on cubic polynomials, inspired by rapidly exploring random tree (RRT) is presented. Some of the path tracking controllers are limited by their ability and hence a standalone collision avoidance system is needed to provide safe maneuvering. Simulations are performed for different test cases with and without obstacles. These simulations help compare safety margin and driving comfort of each path tracking controller that are integrated with the collision avoidance system. Third, different performance metrics like change in acceleration input, change in steering input, error in path tracking, deviation from base frame of track file and lateral and longitudinal margin between ego and target vehicle are presented. To conclude, a set of suitable controllers for heavy articulated vehicles are developed and benchmarked. / Bilindustrin har varit involverad i att göra fordon autonoma till olika nivåer under det senaste decenniet snabbt. Särskilt på marknaden för kommersiella fordon finns det ett stort behov av att få lastbilar att ha en viss nivå av automatisering för att minska beroendet av mänskliga ansträngningar att köra. Detta kan hjälpa till att minska flera olyckor orsakade av mänskliga fel. Intressant nog finns det flera utmaningar och lösningar för att uppnå och implementera autonom körning för lastbilar. Först utforskas ett riktmärke av olika styrarkitekturer som kan få en lastbil att köra autonomt. De valda kontrollerna (Pure Pursuit, Stanley, Linear Quadratic Regulator, Sliding Mode Control och Model Predictive Control) varierar i sin enkelhet i implementering och mångsidighet när det gäller att hantera olika fordonsparametrar och begränsningar. En grundlig jämförelse av dessa vägspårningskontroller utförs med hjälp av flera mätvärden. För det andra presenteras ett system för undvikande av kollisioner baserat på kubiska polynom, inspirerat av snabbt utforskande slumpmässiga träd (RRT). Vissa av vägspårningskontrollerna är begränsade av sin förmåga och därför behövs ett fristående system för att undvika kollisioner för att ge säker manövrering. Simuleringar utförs för olika testfall med och utan hinder. Dessa simuleringar hjälper till att jämföra säkerhetsmarginal och körkomfort för varje vägspårningskontroller som är integrerade med kollisionsundvikande systemet. För det tredje presenteras olika prestandamått som förändring i accelerationsinmatning, förändring i styrinmatning, fel i banspårning, avvikelse från basramen för spårfilen och lateral och longitudinell marginal mellan ego och målfordon. Avslutningsvis utvecklas och benchmarkas en uppsättning lämpliga styrenheter för tunga ledade fordon.

In this thesis, application of linear control methods to control the attitude of a Low-Earth Orbit satellite is studied. Attitude control subsystem is first introduced by explaining attitude determination and control components in detail. Satellite dynamic equations are derived and linearized for controller design. Linear controller and linear quadratic regulator are chosen as controllers for attitude control. The actuators used for control are reaction wheels and magnetic torquers. MATLAB-SIMULINK program is used in order to simulate satellite dynamical model (actual nonlinear model) and controller model. In simulations, the satellite parameters are selected to be similar to the actual BILSAT-1 satellite parameters. In conclusion, simulations obtained from different linear control methods are compared within themselves and with nonlinear control methods, at the same time with that obtained from BILSAT-1 satellite log data.

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

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.

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.

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.

"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

Orientador: Gilberto Pechoto de Melo / Resumo: As metodologias de monitoramento da integridade estrutural baseadas em observadores de estado, em sua grande maioria, utilizam o resíduo obtido a partir da diferença entre a medida e a estimativa de dada resposta dinâmica do sistema para o processo de detecção de danos. Contudo, em determinadas situações, tem-se interesse em realizar o monitoramento através de certa resposta dinâmica que não pode ser medida diretamente. Desta forma, a principal contribuição deste trabalho é propor uma metodologia de detecção de danos para sistemas mecânicos, cujo resíduo é obtido a partir da diferença entre as estimativas do comportamento dinâmico de determinada região do sistema. Estas estimativas são geradas por dois observadores de estado de ordem plena em paralelo, ambos projetados a partir do modelo físico-matemático do sistema em monitoramento sem danos, cujos os ganhos ótimos são determinados pelo método LQR, do inglês Linear Quadratic Regulator. A diferença entre os observadores consiste em serem baseados em conjuntos de medidas distintos. Simulações computacionais são apresentadas para demonstrar a aplicação desta metodologia, de maneira que são discutidas as vantagens e desvantagens em monitorar o sistema utilizando diferentes tipos de força de excitação. Os resultados obtidos são satisfatórios para a detecção dos tipos de dano considerados neste trabalho. / Abstract: Structural health monitoring methodologies based on state observers, for the most part, use the residual obtained from the di erence between the measurement and the estimate of the given dynamic response of the system to the damage detection process. However, in certain situations, it is interesting to carry out the monitoring through a certain dynamic response that can not be measured directly. In this way, the main contribution of this work is to propose a methodology of damage detection for mechanical systems, whose residue is obtained from the di erence between the estimates of the dynamic behavior of a certain region of the system. These estimates are generated by two parallel full-order state observers, both designed from the physical-mathematical model of the monitoring system without damages, whose optimal gains are determined by the LQR (Linear Quadratic Regulator) method. The di erence between observers is that they are based on di erent sets of measures. Computational simulations are presented to demonstrate the application of this methodology, so that the advantages and disadvantages of monitoring the system using di erent types of excitation force are discussed. The results obtained are satisfactory for the detection of the types of damage considered in this work. / Mestre

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-22T13:19:28Z No. of bitstreams: 1 Patricia Moraes Rêgo.pdf: 1511056 bytes, checksum: 21108136b08107eeb212f5d74ed79ef7 (MD5) / Made available in DSpace on 2017-08-22T13:19:28Z (GMT). No. of bitstreams: 1 Patricia Moraes Rêgo.pdf: 1511056 bytes, checksum: 21108136b08107eeb212f5d74ed79ef7 (MD5) Previous issue date: 2007-08-03 / FAPEMA / In this work are proposed models and a convergence analysis of a hierarchical genetic algorithm for the linear quadratic regulator design loop recovery through LQG/LTR controllers. Models are oriented to the weighting and covariance matrices searching of the performance indices of the LQR and LQG design, respectively, and to the selection of the matrices for the LQR design loop recovery gain. The convergence analysis aims at promoting the enhancement of the algorithm performance, as well as to generate satisfactory solutions and speed up the convergence time. The algorithm performance is evaluated with respect to the e ects of an elitist strategy embodied into the algorithm and to variations in the values of some given parameters of the algorithm. The proposed methodology is evaluated in a multi-variable dynamical system representing an aircraft. / Propõe-se neste trabalho os modelos e a análise de convergência de um algoritmo genético hierárquico para recuperação da malha de projeto do regulador linear quadrático por controladores LQG/LTR (Linear Quadratic Gaussian/Loop Transfer Recovery). Os modelos dedicam-se à busca das matrizes de ponderações e covariâncias dos índices de desempenho dos projetos de controladores LQR (Linear Quadratic Regulator) e LQG (Linear Quadratic Gaussian), respectivamente, e à seleção de matrizes de ajuste para o ganho de recuperação da malha do projeto LQR. O objetivo da análise de convergência é promover melhorias no desempenho do algoritmo no sentido de gerar soluções satisfatórias e acelerar o tempo de convergência. O desempenho do algoritmo é avaliado em relação aos efeitos de uma estratégia elitista incorporada ao algoritmo e à variações nos valores de determinados parâmetros do algoritmo. A metodologia proposta é avaliada em um sistema dinâmico multivariável que representa uma aeronave.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-09-04T13:10:41Z No. of bitstreams: 1 Marcio Eduardo.pdf: 7939176 bytes, checksum: 3b90c4b32aeabafd3b87e4f3c36d2ed6 (MD5) / Made available in DSpace on 2017-09-04T13:10:41Z (GMT). No. of bitstreams: 1 Marcio Eduardo.pdf: 7939176 bytes, checksum: 3b90c4b32aeabafd3b87e4f3c36d2ed6 (MD5) Previous issue date: 2014-08-21 / The technique of linear control based on the minimization of a quadratic performance index using the second method of Lyapunov to guarantee the stability of the system, if this is controllable and observable. however, this technique is inevitably necessary to find the solution of the HJB or Riccati equation. The control system design online need, real time, to adjust your feedback gain to maintain a certain dynamic, it requires the calculation of the Riccati equation solution in each sampling generating a large computational load that can derail its implementation. This work shows an intelligent control system design that meets the optimal or suboptimal control action from the sensory data of process states and the instantaneous cost observed after each state transition. To find this optimal control action or policy, the approximate dynamic programming and adaptive critics are used, based on the parameterizations given by the problem of linear quadratic regulator (LQR), but without explicitly solving the associated Riccati equation. More specifically, the LQR problem is solved by four different methods which are the Dynamic Programming Heuristic, the Dual Heuristic Dynamic Programming, Action Dependent Dynamic Programming Heuristic and Action Dependent Dual Heuristic Dynamic Programming algorithms. However, these algorithms depend on knowledge of the value functions to derive the optimal control actions. These value functions with known structures have their parameters estimated using the least mean square family and Recursive Least Squares algorithms. Two processes that have the Markov property were used in the computational validation of the algorithms adaptive critics implemented, one corresponds to the longitudinal dynamics of an aircraft and the other to an electrical circuit. / A técnica de controle linear baseado na minimização de um índices de desempenho quadrático utilizando o segundo método de Liapunov garante a estabilidade do sistema, se este for controlável e observável. Por outro lado, nessa técnica inexoravelmente é necessário encontrar a solução da Equação Hamilton-Jacobi-Bellman (HJB) ou Riccati. Em projeto de sistema de controle online que necessita, em tempo real, alterar seus ganhos de retroação para manter uma certa dinâmica, impõe o cálculo da solução da equação de Riccati em cada instante de amostragem gerando uma grande carga computacional que pode inviabilizar sua implementação. Neste trabalho, mostra-se o projeto de um sistema de controle inteligente que encontra a ação de controle ótima ou subótima a partir de dados sensoriais dos estados do processo e do custo instantâneo observados após cada transição de estado. Para encontrar essa ação de controle ou política ótima, a programação dinâmica aproximada ou críticos adaptativos são utilizados, tendo como base as parametrizações dado pelo problema do regulador linear quadrático (LQR), mas sem resolver explicitamente a equação de Riccati associada. Mais especificamente, o problema do LQR é resolvido por quatro métodos distintos que são os algoritmos de Programação Dinâmica Heurística, a Programação Dinâmica Heurística Dual, a Programação Dinâmica Heurística Dependente de Ação e a Programação Dinâmica Heurística Dual Dependente de Ação. Entretanto, esses algoritmos dependem do conhecimento das funções valor para, assim, derivar as ações de controle ótimas. Essas funções valor com estruturas conhecidas tem seus parâmetros estimados utilizando os algoritmos da família dos mínimos quadrados médios e o algoritmo de Mínimos Quadrados Recursivo. Dois processos que obedecem à propriedade de Markov foram empregados na validação computacional dos algoritmos críticos adaptativos, um corresponde à dinâmica longitudinal de uma aeronave e o outro à de um circuito elétrico.