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Mechanical systems operate under parametric and external excitation uncertainties. The polynomial chaos approach has been shown to be more efficient than Monte Carlo approaches for quantifying the effects of such uncertainties on the system response. This work uses the polynomial chaos framework to develop new methodologies for the simulation, parameter estimation, and control of mechanical systems with uncertainty. This study has led to new computational approaches for parameter estimation in nonlinear mechanical systems. The first approach is a polynomial-chaos based Bayesian approach in which maximum likelihood estimates are obtained by minimizing a cost function derived from the Bayesian theorem. The second approach is based on the Extended Kalman Filter (EKF). The error covariances needed for the EKF approach are computed from polynomial chaos expansions, and the EKF is used to update the polynomial chaos representation of the uncertain states and the uncertain parameters. The advantages and drawbacks of each method have been investigated. This study has demonstrated the effectiveness of the polynomial chaos approach for control systems analysis. For control system design the study has focused on the LQR problem when dealing with parametric uncertainties. The LQR problem was written as an optimality problem using Lagrange multipliers in an extended form associated with the polynomial chaos framework. The solution to the Hâ problem as well as the H2 problem can be seen as extensions of the LQR problem. This method might therefore have the potential of being a first step towards the development of computationally efficient numerical methods for Hâ design with parametric uncertainties. I would like to gratefully acknowledge the support provided for this work under NASA Grant NNL05AA18A. / Ph. D.

This thesis presents a dedicated GPS-denied landing system designed for electric vertical takeoff and landing (eVTOL) aircraft. The system employs active fiducial light pattern localization (AFLPL), which provides highly accurate and reliable navigation during critical landing phases. AFLPL utilizes images of a constellation comprised of modulating infrared lights strategically positioned on the landing site, to determine the aircraft pose through the use of a perspective-n-point (PnP) solver. The AFLPL system underwent thorough development, enhancement, and implementation to address and demonstrate its potential in navigation and its inherent limitations. A proposed method addresses the limitations of AFLPL by using an extended Kalman filter (EKF) to fuse PnP camera pose estimates with sensor measurements from an inertial measurement unit (IMU), attitude heading reference system (AHRS), and optional global positioning system (GPS). The EKF estimation is reported to significantly enhance the accuracy, reliability, and update frequency of the aircraft state estimation. To refine and validate the AFLPL and EKF algorithms, a simulation was developed, consisting of an eVTOL executing a glideslope landing trajectory. Furthermore, a hardware system consisting of a multirotor and infrared light ground units was implemented to test these methods under real-world conditions. This research culminated in the successful demonstration of the AFLPL-based estimation system's efficacy through an autonomous, GPS-denied landing flight test, affirming its potential to improve the navigation and control of eVTOL aircraft lacking access to GPS information.

With the rising popularity of ROVs and other UV solutions, more robust and high performance controllers have become a necessity. A model of the ROV or UV can be a valuable tool during control synthesis. The main objective of this thesis was to use a model in design and development of controllers for an ROV. In this thesis, an ROV from Blue Robotics was used. The ROV was equipped with 6 thrusters placed such that the ROV was capable of moving in 6-DOFs. The ROV was further equipped with an IMU, two pressure sensors and a magnetometer. The ROV platform was further developed with EKF-based sensor fusion, a control system and manual control capabilities. To model the ROV, the framework of Fossen (2011) was used. The model was estimated using two different methods, the prediction-error method and an EKF-based method. Using the prediction-error method, it was found that the initial states of the quaternions had a large impact on the estimated parameters and the overall fit to validation data. A Kalman smoother was used to estimate the initial states. To circumvent the problems with the initial quaternions, an \abbrEKF was implemented to estimate the model parameters. The EKF estimator was less sensitive to deviations in the initial states and produced a better result than the prediction-error method. The resulting model was compared to validation data and described the angular velocities well with around 70 % fit. The estimated model was used to implement feedback linearisation which was used in conjunction with an attitude controller and an angular velocity controller. Furthermore, a depth controller was developed and tuned without the use of the model. Performance of the controllers was tested both in real tests and simulations. The angular velocity controller using feedback linearisation achieved good reference tracking. However, the attitude controller could not stabilise the system while using feedback linearisation. Both controllers' performance could be improved further by tuning the controllers' parameters during tests. The fact that the feedback linearisation made the ROV unstable, indicates that the attitude model is not good enough for use in feedback linearisation. To achieve stability, the magnitude of the parameters in the feedback linearisation were scaled down. The assumption that the ROV's center of rotation coincides with the placement of the ROV's center of gravity was presented as a possible source of error. In conclusion, good performance was achieved using the angular velocity controller. The ROV was easier to control with the angular velocity controller engaged compared to controlling it in open loop. More work is needed with the model to get acceptable performance from the attitude controller. Experiments to estimate the center of rotation and the center of gravity of the ROV may be helpful when further improving the model.

Les entraîneurs en sports acrobatiques disposent de peu d’outils permettant d’améliorer leur compréhension des saltos vrillés et la performance des athlètes. L’objectif de ce mémoire était de développer un environnement graphique de simulation numérique réaliste et utile des acrobaties aériennes. Un modèle composé de 17 segments et de 42 degrés de liberté a été développé et personnalisé à une athlète de plongeon. Un système optoélectronique échantillonné à 300 Hz a permis l’acquisition de huit plongeons en situation réelle d’entraînement. La cinématique articulaire reconstruite avec un filtre de Kalman étendu a été utilisée comme entrée du modèle. Des erreurs quadratiques moyennes de 20° (salto) et de 9° (vrille) entre les performances simulées et réelles ont permis de valider le modèle. Enfin, une formation basée sur le simulateur a été offerte à 14 entraîneurs en sports acrobatiques. Une augmentation moyenne de 11 % des résultats aux questionnaires post-test a permis de constater le potentiel pédagogique de l’outil pour la formation. / Coaches need tools to better understand the mechanics of twisting somersaults and improve their knowledge and their athletes’ performance. The aim of this thesis was to provide them with a computer simulation model of aerial movements. An elite diver was modelled as a 17-segment 42-degree of freedom angle-driven model. The model was personalised to the diver so that simulation outputs could be compared with her actual performance. Input data were recorded by a 17-camera motion capture system sampled at 300 Hz. The joint angle time histories were reconstructed using an extended Kalman filter. The model was successfully evaluated and shown to produce realistic movements, with overall root-mean-square error of 20° (somersault) and 9° (twist) between reconstructed body kinematics and the corresponding simulations for eight dives. Finally, a workshop based on the simulation was offered to coaches and showed potential to improve their knowledge since the mean post-test result was increased by 11 %.

A demanda por mais flexibilidade nas fábricas e serviços originou um aumento no volume de operações internas de carga e descarga, devido à maior diversidade dos elementos transportados. Logo, na busca por um fluxo de materiais mais eficiente, as empresas passaram a investir em soluções tecnológicas, entre elas, o uso de Automated Guided Vehicles (AGVs), por conta do custo mais atrativo e do avanço em relação aos primeiros AGVs, que até então dependiam de uma infraestrutura adicional para suportar a navegação. Muitos AGVs modernos possuem movimentação livre e são orientados por sistemas que utilizam sensores para interpretar o ambiente, sendo assim, tornar os AGVs autônomos despertou o interesse de pesquisadores na área de robótica móvel para o desenvolvimento de sistemas capazes de auxiliar e coordenar a navegação. Novas técnicas de localização, tal como a localização baseada em marcadores reflexivos, e a construção de armazéns com layouts estruturados para a navegação viabilizaram o uso de AGVs autônomos, entretanto sua utilização em armazéns existentes ainda é um desafio. Neste contexto, o Laboratório de Robótica Móvel (LabRom) do Grupo de Mecatrônica da EESC/USP, através do projeto do Armazém Inteligente, tem pesquisado os problemas de: roteamento, gerenciamento das baterias, navegação e auto-localização. Robôs autônomos precisam de um sistema de auto-localização eficiente e preciso para navegar com segurança, o qual depende de um mapa e da interpretação do ambiente utilizando sensores embarcados. Para alcançar esse objetivo este trabalho propõe um Sistema de Auto-localização baseado no Extended Kalman Filter (EKF) como solução. O sistema, desenvolvido em linguagem C, interage com outros dois sistemas: roteamento e navegação e foi implementado em um armazém simulado utilizando o software Player/Stage, mostrando ser confiável no fornecimento de uma estimativa de localização baseada em odometria e landmarks com localização conhecida. O sistema foi novamente testado utilizando a odometria de um robô móvel Pioneer P3-AT e os valores de um sensor de medição laser 2D SICK LMS200 extraídos de um ambiente indoor real. Para este teste foi construído um feature-based map a partir de um desenho de planta baixa no formato CAD e utilizou-se o algoritmo de segmentação Iterative End-Point Fit (IEPF) para interpretar o ambiente. Os resultados mostraram que as vantagens oferecidas pelas características padronizadas de um ambiente indoor, semelhante a um armazém, podem viabilizar o uso do Sistema de Auto-localização em armazéns existentes. / The demand for more flexibility in factories and services led to an increase in the volume of internal operations of loading and unloading, due to the greater diversity of elements transported. Hence, in the search for a more efficient materials flow, companies went to invest in technology solutions, among them, the use of Automated Guided Vehicles (AGVs), on account of the more attractive cost and improvement over the first AGVs, which hitherto depended of an additional infrastructure to support navigation. Many modern AGVs have free movement and are guided by systems that use sensors to interpret the environment, thus make AGVs autonomous aroused the interest of researchers in the mobile robotics field to development of systems able to assist and coordinate the navigation. New localization techniques, such as localization based on reflective markers, and the construction of warehouses with structured layouts for navigation did feasible the use of autonomous AGVs, however its use in existing warehouses is still a challenge. In this context, the Mobile Robotics Lab (LabRom) of the Mechatronics Group of EESC/USP, through the Intelligent Warehouse Project, has researched the problems: routing, battery management, navigation and self-localization. Autonomous robots need an efficient and accurate self-localization system to safely navigate, which depends on one map and of the interpretation of the environment using embedded sensors. To achieve this goal, this work proposes a Self-Localization System based on the Extended Kalman Filter (EKF) as a solution. The system, developed in C language, interacts with two other systems: routing and navigation and was implemented in a simulated warehouse using the Player/Stage software, showing to be reliable in providing an estimative of localization based on odometry and landmarks with known localization. The system was again tested using the odometry of mobile robot Pioneer P3-AT and the values of a 2D Laser Rangefinder SICK LMS200 extracted from a real indoor environment. For this test was built a feature-based map from a floor plan design in CAD format and was used the segmentation algorithm Iterative End-Point Fit (IEPF) to interpret the environment. The results showed that the advantages offered by the standard features of indoor environment, like a warehouse, can enable the use of the Self-Localization System on the existing warehouses.

Neste trabalho, é abordado o problema da navegação de um veículo submarino autônomo. São propostos estimadores de estado que realizam fusão sensorial baseada em Filtro Estendido de Kalman. Esses estimadores de estado empregam as medidas dos seguintes sensores: uma unidade de medição inercial, um sensor de velocidade por efeito Doppler, um profundímetro e uma bússola. Primeiramente foi projetado um estimador de estados para o AUV Pirajuba, onde a estimação da orientação do veículo é realizada de forma desacoplada à estimação da velocidade e posição do veículo. Em seguida, foram desenvolvidos dois estimadores de estado que estimam orientação, velocidade e profundidade do veículo de forma acoplada. Para o projeto e testes dos estimadores mencionados anteriormente, foi empregada uma base de dados contendo um registro de medições reais dos sensores do veículo submarino autônomo Pirajuba, durante testes de campo no lago de uma represa. Os resultados dos testes validaram os estimadores de estado propostos nesse trabalho. Por último, foi realizada uma análise comparativa dos estimadores de estado mencionados. / This work concerns the navigation problem of an autonomous underwater vehicle. Some state estimators using sensorial fusion were proposed, the sensorial fusion is based in an Extended Kalman Filter. The state estimators are fed by measurements of the following sensors: an inertial measurements unit, a velocity sensor by Doppler effect, a depthmeter and a compass. In the first version of the EKF algorithm, the vehicles attitude estimation was decoupled from the vehicle velocity estimation. The second version considers the coupling between linear velocity and the attitude in the vehicle reference frame, taking the velocity reading for correction of the filter estimates. Finally, in the third version, the coupling between position and attitude is also considered, but the correction of the filters estimates is based on the depth readings. Experiments for supporting the design and validation of the navigation algorithms were based on a database constructed with motion measurements during the AUV maneuvers in the north coast of Sao Paulo, and the Guarapiranga lake in the São Paulo city. This work presents a comparative analysis of those algorithms.

Coordenação de robôs móveis é um tópico importante de pesquisa dado que existem tarefas que podem ser desenvolvidas de forma mais eficiente e com menor custo por um grupo de robôs do que por um só robô. Nesta dissertação é apresentado um estudo sobre coordenação de robôs móveis para o problema de navegação em ambientes externos. Para isso, foi desenvolvido um sistema de localização utilizando os dados de odometria e do receptor GPS, e um sistema de desvio de obstáculos para planejar a trajetória livre de obstáculos. Os movimentos coordenados foram realizados em função de um líder e qualquer robô da formação pode assumir a liderança. A liderança é assumida pelo robô que ultrapassar a distância mínima a um obstáculo. Movimentos estáveis são gerados através de uma lei de controle descentralizada baseada nas coordenadas dos robôs. Para garantir a estabilidade da formação quando há alternância de líder ou remoção de robôs, foi feito controle tolerante a falhas para um grupo de robôs móveis. O controle tolerante a falhas é baseado em controle H \'INFINITO\' por realimentação da saída de sistemas lineares sujeitos a saltos Markovianos para garantir a estabilidade da formação quando um dos robôs é perdido durante o movimento coordenado. Os resultados do sistema de localização mostram que o uso de filtro robusto para a fusão de dados produz uma melhor estimativa da posição do robô móvel. Os resultados também mostram que o sistema de desvio de obstáculos é capaz de gerar uma trajetória livre de obstáculos em ambientes desconhecidos. E por fim, os resultados do sistema de coordenação mostram que o grupo de robôs mantém a formação desejada percorrendo a trajetória de referência na presença de distúrbios ou quando um robô sai da formação. / Coordination of mobile robots is an important topic of research because there are tasks that may be too difficult for a single robot to perform alone, these tasks can be performed more efficiently and cheaply by a group of mobile robots. This dissertation presents a study on the coordination of mobile robots to the problem of navigation in outdoor environments. To solve this problem, a localization system using data from odometry and GPS receiver, and an obstacle avoidance system to plan the collision-free trajectory, were developed. The coordinated motions are performed by the robots that follow a leader, and any robot of the formation can assume the leadership. The leadership is assumed by a robot when it exceeds the threshold distance to an obstacle. Stable motions are generated by a decentralized control law based on the robots coordinates. To ensure the stability formation when there is alternation of leader or one of the robots is removed, we made a fault tolerant control for a group of mobile robots. The fault tolerant approach is based on output feedback H \'INFINITE\' control of Markovian jump linear systems to ensure stability of the formation when one of the robots is lost during the coordinated motion. The results of the localization system show that the use of robust filter for data fusion produces a better estimation of the mobile robots position. The results also show that the obstacle avoidance system is capable of generating a path free from obstacles in unknown environments. Finally, the results of the coordination system show that the group of robots maintain the desired formation along the reference trajectory in the presence of disturbance or removal of one of them.

La pile à combustible (PàC) est une source d’énergie qui produit de l’électricité à partir de l’hydrogène et de l’oxygène.Elles sont très prometteuses pour la production d'énergie électrique. Néanmoins, la PàC souffre encore d’imperfections limitant ainsi sa commercialisation à grande échelle, tout particulièrement pour les applications de transport.C’est pourquoi, l’hybridation des différentes sources d’énergies est devenue une réalité pour les applications non-stationnaires telles que les véhicules tout électriques.Cependant ces applications nécessitent des solutions de gestion de l’énergie fiables prenant en compte toutes les contraintes du système électrique hybride.Par conséquent, le développement de plateforme de validation est nécessaire.Dans ce contexte, le Hardware In the Loop (HIL) est une technique très prometteuse, où une partie d’un système réel peut être remplacée par un système virtuel tout en respectant la communication entre ces sous-systèmes physiques et virtuels.Ce mémoire détaille des modèles dynamiques d'une pile à combustible échangeuse de proton (PEMFC) hybridée à des supercondensateurs.Par ailleurs, on détaille la gestion d’énergie entre ces deux sources, ainsi que le pronostic de la pile basé d’une part d’un filtre de Kalman étendu (EKF) pour l’estimation de l’état de santé (SoH) réel de la pile, et d’autre part, de la méthode Inverse First Order Reliability Method (IFORM) en vue d’estimer la durée de vie utile restante de la pile, tout cela dans une approche Hardware-In-The-Loop (HIL). / The fuel cell is a source of energy that generates electricity from hydrogen and oxygen.They are very promising candidates for the production of electric power.Nevertheless, the fuel cell still suffers from imperfections limiting its commercialization on a full scale, in particular for transport applications.This is the reason why, hybridization of different energy sources has become a reality for non-stationary applications such as all-electric vehicles.However, these applications require reliable energy management solutions that take into account all the constraints of the hybrid electrical system.Therefore, the development of validation platform is necessary.In this context, the Hardware In the Loop (HIL) is a very promising technique, where part of a real system can be replaced by a virtual system while respecting the communication between these physical and virtual subsystems.This document details the dynamic models of a proton exchange membrane fuel cell (PEMFC) associated with supercapacitors.Furthermore, the energy management between these two sources and the prognostic of the fuel cell composed of a extenced Kalman Filter filter (EKF) for the estimation of the real state of health (SoH) of the stack and, on the other hand, of the Inverse First Order Reliability Method (IFORM) in order to estimate the remaining useful life of the stack, all implemented in an FPGA control board in a Hardware-In-The-Loop (HIL) context.