Biological phenomena found in nature can be learned and customized to obtain innovative engineering solutions. In recent years, biologists found that birds and bats use their mechanoreceptors to sense the airflow information and use this information directly to achieve their agile flight performance. Inspired by this phenomenon, an attitude control system for micro air vehicles using rich amount of airflow sensor information is proposed, designed and tested. The dissertation discusses our research findings on this topic. First, we quantified the errors between the calculated and measured lift and moment profiles using a limited number of micro pressure sensors over a straight wing. Then, we designed a robust pitching controller using 20 micro pressure sensors and tested the closed-loop performance in a simulated environment. Additionally, a straight wing was designed for the pressure sensor based pitching control with twelve pressure sensors, which was then tested in our low-speed wind tunnel. The closed-loop pitching control system can track the commanded angle of attack with a rising time around two seconds and an overshoot around 10%. Third, we extended the idea to the three-axis attitude control scenarios, where both of the pressure and shear stress information are considered in the simulation. Finally, a fault tolerant controller with a guaranteed asymptotically stability is proposed to deal with sensor failures and calculation errors. The results show that the proposed fault tolerant controller is robust, adaptive, and can guarantee an asymptotically stable performance even in case that 50% of the airflow sensors fail in flight.

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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.

A robust model predictive control algorithm is presented that explicitly handles multiplicative, or parametric, uncertainty in linear discrete models over a finite horizon. The uncertainty in the predicted future states and inputs is bounded by polytopes. The computational cost of running the controller is reduced by calculating matrices offline that provide a means to construct outer approximations to robust constraints to be applied online. The robust algorithm is extended to problems of uncertain models with an allowed probability of violation of constraints. The probabilistic degrees of satisfaction are approximated by one-step ahead sampling, with a greedy solution to the resulting mixed integer problem. An algorithm is given to enlarge a robustly invariant terminal set to exploit the probabilistic constraints. Exponential basis functions are used to create a Robust MPC algorithm for which the predictions are defined over the infinite horizon. The control degrees of freedom are weights that define the bounds on the state and input uncertainty when multiplied by the basis functions. The controller handles multiplicative and additive uncertainty. Robust MPC is applied to the problem of wind turbine control. Rotor speed and tower oscillations are controlled by a low sample rate robust predictive controller. The prediction model has multiplicative and additive uncertainty due to the uncertainty in short-term future wind speeds and in model linearisation. Robust MPC is compared to nominal MPC by means of a high-fidelity numerical simulation of a wind turbine under the two controllers in a wide range of simulated wind conditions.

The Linear Quadratic Regulator (LQR) is an optimal control approach which aims to drive states of a linear system to its origin through the minimization of a quadratic cost functional. Such an approach has been widely successful for both theoretical and practical applications. However, when such controllers are subject to uncertainties, optimal closed-loop performance cannot be obtained since robustness properties are no longer guaranteed. Guaranteed Cost Controllers (GCC) presents robust asymptotic stability and provides a guaranteed upper bound to a quadratic cost function. Such method addresses the lack of performance guarantees of the LQR. Meanwhile, Model Predictive Control (MPC) is a class of optimization-based control algorithms that use an explicit model of the controlled system to predict its future states. The MPC can be as a generalization of the LQR for constrained linear systems. Therefore, it equally suffers from a lack of robustness guarantees when the system is subject to uncertainties. Robust MPC (RMPC) approaches were proposed to address MPCs poor closed-loop performance subject to uncertainties. Its objective is to obtain a control input sequence that simultaneously minimizes a cost function and guarantees the feasibility of system states and control inputs, for a system subject to the worst-case disturbance within an uncertainty set. Autonomous vehicles have gained increasing interest from both the industry and research communities in recent years. An essential aspect in the design of automotive control systems is to ensure the controller is stable and has acceptable performance within the entire operational envelope which it is designed to operate. In the case of autonomous vehicles, where there is no human driver as a fallback, it is of utmost importance to ensure the safe operations of the control system and its capability to avoid saturating the handling limits of the vehicle. In this thesis, we propose Guaranteed Cost Controller approaches for both unconstrained and constrained linear systems subject to multiplicative structured norm-bounded uncertainties and present the application of such a controller to the lateral control problem of autonomous vehicles up to the tire saturation limits. / O Regulador Quadrático Linear (Linear Quadratic Regulator, LQR) é uma abordagem de controle ótimo que visa conduzir estados de um sistema linear à sua origem através da minimização de um custo funcional quadrático. Tal abordagem tem sido amplamente bem sucedida para aplicações teóricas e práticas. No entanto, não é possível obter o desempenho ótimo de malha fechada quando esses controladores são sujeitos a incertezas no sistema em decorrência de suas propriedades de robustez não serem garantidas. Controladores de Custo Garantido (Guaranteed Cost Control, GCC) visam abordar a falta de garantia de desempenho do LQR, neste caso. Esses controladores apresentam estabilidade assintótica robusta e fornecem um custo garantido de pior caso para uma função de custo quadrático. O Controle Preditivo de Modelo (Model Predictive Control, MPC) é uma classe de algoritmos de controle baseados em otimização que usa um modelo explícito do sistema controlado para prever seus estados futuros. Uma possível interpretação do MPC é uma generalização do LQR para sistemas lineares com restrições de estado e entrada de controle. Portanto, essa abordagem sofre igualmente da falta de garantias de robustez quando o sistema é sujeito a incertezas. As abordagens de MPC Robustas (Robust MPC, RMPC) foram propostas para abordar o desempenho de malha fechada do MPC sujeito a incertezas no sistema. Seu objetivo é obter uma sequência de entrada de controle que minimize simultaneamente uma função de custo e garanta que os estados do sistema e as entradas de controle estão contidos dentro das restrições para um sistema sujeito à pior das perturbações dentro de um conjunto admissível de incertezas. Pesquisas voltadas para veículos autônomos ganharam crescente interesse nos últimos anos, tanto da indústria automobilística quanto da comunidade acadêmica. Um aspecto essencial no projeto de sistemas de controle automotivo é a garantia de estabilidade e desempenho do controlador dentro de todo o envelope operacional ao qual ele foi projetado para operar. No caso de veículos autônomos, onde não há motoristas humanos para lidar com casos de falha do sistema, é de suma importância assegurar as operações seguras do sistema de controle e sua capacidade de evitar a saturação dos limites de manuseio do veículo. Nesta tese, propomos abordagens GCC para sistemas lineares restritos e irrestritos, sujeitos a incertezas estruturadas contidas por norma e apresentamos a aplicação de tais controladores ao problema de controle lateral de veículos autônomos até os limites de saturação dos pneus.

Les énergies renouvelables représentent aujourd'hui un domaine de développement de plus en plus important, au vu de la consommation énergétique mondiale et de ses conséquences désastreuses sur l'environnement. Les différents accords politiques, notamment l'accord de Paris, ne peuvent à eux seuls apporter une solution définitive au changement climatique actuel. Les contraintes imposées par la réduction des émissions de CO_2 et l’augmentation du prix du pétrole dans l’industrie maritime ont poussé Yves Parlier à lancer le projet « beyond the sea » dans le but de développer des cerfs-volants (kites) dédiés à la propulsion auxiliaire des navires. L'objectif principal de cette étude est donc la modélisation et la commande robuste d'une aile de kite en vol dynamique. Le but à terme étant l'élaboration d'un pilote automatique dédié à la traction d'un navire par kite. Un modèle « point-masse » du kite est proposé afin de comprendre et contrôler sa dynamique. Les différents paramètres du modèle sont estimés à partir de données expérimentales obtenues lors d’essais en conditions réelles. Des simulations en boucle ouverte sont proposées afin de valider la cohérence du modèle. Pour effectuer un vol dynamique, une trajectoire en forme de huit est définie dans la fenêtre de vol. La position, la taille et l’orientation de cette trajectoire sont des paramètres ajustables par l’utilisateur. Un algorithme de suivi de trajectoire est développé permettant ensuite de synthétiser une loi de commande robuste intégrant le modèle du kite. Ce pilote automatique permet donc d’effectuer une grande variété de trajectoires pour toute une gamme de vitesses de vent. Enfin, des simulations en boucle fermée montrant les performances théoriques du système mettent en évidence l’intérêt de la propulsion auxiliaire des navires par kite. / The need in reducing the CO_2 emissions and the increase of oil prices affect all transportation industries and especially the maritime industry. This has led to the search for more energy-saving ship propulsion systems. Taking advantage of wind energy by using tethered wings, or kites, as an alternative propulsion source can be an effective solution. The "beyond the sea" project, led by Yves Parlier, aims to provide ships an alternative green energy source. In most wind conditions, compared to a static flight, a dynamic motion of a tethered wing with an eight-shaped pattern can provide sufficient force through traction to tow a ship. Therefore, the main objective of this study is the modeling and robust control of a tethered kite in dynamic flight. To this end, a point mass model is first used to describe the kite dynamics. The model parameters are estimated from experimental data and the aerodynamic coefficients are identified using data from a quasi-static flight. Open loop simulations are conducted to verify the kite behavior and the overall coherence of the model. To ensure a dynamic flight, an eight-shaped trajectory is defined within the wind window. Its position, size, orientation and direction are all adjustable parameters. A path-following strategy is then developed in order to design a robust control law including the kite model. This allows the system to be used in different trajectories with a wide range of wind speeds. Closed-loop simulations are presented to show the efficiency of the path-following algorithm, and the various theoretical performances obtained shows the efficiency of a kite dedicated to vessels auxiliary propulsion.

Les problématiques de recherche abordées dans cette thèse concernent la conceptualisation, la modélisation et la commande générique des robots mobiles lors de leur évolution en milieux extérieurs et en présence de glissement pour des applications de suivi de précision. Ainsi, ce mémoire synthétise dans un premier temps les développements et résultats obtenus lors du suivi de trajectoire (localisation absolue), puis synthétise ensuite ceux obtenus lors de suivi de structure et de cible (localisation relative). Une dernière partie introduit un concept de plateforme robotique reconfigurable et sa commande associée pour adapter l’assiette et les dimensions du châssis en fonction de la topographie du terrain.Pour chaque application de suivi, ce mémoire présente un panel de lois de commande originales pour des robots différentiels, à un train et à deux trains directeurs. Chaque modalité de commande est présentée en quatre étapes : modélisation, estimation, commande et expérimentations. La première contribution majeure de la thèse concerne l’estimation du glissement. Cette dernière est adaptative et basée modèle. Elle intègre la modélisation cinématique étendue seule ou couplée à la modélisation dynamique du robot mobile pour assurer une estimation intègre quels que soient la vitesse, les phénomènes dynamiques rencontrés et la nature du sol. La seconde contribution majeure concerne le développement d’une stratégie de commande générique pour les robots mobiles. Cette stratégie est basée sur le principe de la commande en cascade (ou par backstepping) et est déclinée dans ce mémoire à travers un panel de lois de commande. Cette méthodologie de commande, lorsqu’elle est associée à l’observation du glissement précédent, permet d’obtenir des performances de suivi accrues quel que soit le contexte rencontré. L’ensemble des algorithmes ont été validés en simulation et/ou expérimentalement à l’aide de différentes plateformes robotiques en contextes réels. / This work is focused on the conceptualization, the modeling and the genericcontrol of mobile robots when moving in off-road contexts and facing slipperyterrains, especially for very accurate tracking and following applications. Thisthesis summarizes the proposed methods and the obtained results to addressthis research issue, first for path following applications (absolute localization)and then for edge and target tracking applications (relative localization). A finalsection of this thesis introduces an adaptive robotic concept and its associatedcontroller allowing the adaptation of the pose (position and orientation) of thechassis with respect to the environment topography.For each application, this thesis introduces a panel of innovative control algorithmsfor controlling skid-steering, two-wheel steering and four-wheel steeringmobile robots. Each algorithm of the panel is described, in this thesis, infour steps : modeling, estimation, control and experiments.The first main contribution of this thesis deals with the slippage estimation.The latter is adaptive and model-based. It also includes the extended kinematicmodeling only or together with the dynamic modeling of the mobile robot toensure a robust estimation of the slippage whatever the speed of the robot, encountereddynamic phenomena or even ground characteristics.The second main contribution deals with the design of a generic control approachfor mobile robots when path following and target tracking. The proposedstrategy is mostly based on a backstepping method and is illustrated inthis thesis via a panel of control laws. When combining this proposed controlapproach with the slippage estimation described above, significant improvedtracking and following performances are obtained (in term of stability, repeatability,accuracy and robustness) whatever the encountered context.All algorithms have been tested and validated through simulations and/orfull-scale experiments, indoor and off-road, with different mobile robots.

Le travail présenté dans cette thèse s'inscrit dans le cadre de l'étude de l'efficacité de la commande CRONE avec effet anticipatif robuste dans les problèmes de poursuite et de régulation pour les systèmes monovariables et multivariables. L'objectif de l'anticipation est de concevoir des algorithmes de commande capables de minimiser un critère quadratique basé sur l'erreur entre la sortie du système et le signal de référence avec modération du niveau du signal de commande. La solution proposée repose sur l'association d'une commande robuste de type feedback à une commande anticipative de type feedforward. L'action feedforward utilise un filtre anticipatif synthétisé dans le domaine fréquentiel en utilisant des critères d'optimalité et de contraintes de type $mathcal{H}_2$ et $mathcal{H}_infty$. La réduction du nombre de paramètres du filtre anticipatif en utilisant une période d'échantillonnage plus lente a été traitée. Des exemples académiques illustrent les développements théoriques. Une application pratique sur un système de régulation hydraulique a montré la pertinence de cette nouvelle approche. / The work presented in this thesis is part of the study of the effectiveness of CRONE control with anticipative effect in the problems of tracking and control for monovariable and multivariable systems. The anticipation objective is to design a control algorithm that minimizes a quadratic error between the reference and the output of the system and at the same time achieve a good level of the control signal. The proposed solution combines a robust feedback control with a feedforward control. The feedforward action uses optimality criteria and an anticipative filter which is designed in the frequency domain using a mix of $mathcal{H}_2$ et $mathcal{H}_infty$ constraints. The reduction of the number of parameters of the anticipative filter by using a slower sampling period for the anticipative filter is treated. Academic examples highlight the theoretical developments. A practical application on a hydraulic control system has shown the efficiency of the developed approach.