Dans ce travail, l’optimisation de la propulsion et de la commande des AUV (Autonomous Underwater Vehicles en anglais) est développée. Le modèle hydrodynamique de l’AUV est examiné. Egalement, son système de propulsion est étudié et des modèles pour des solutions de propulsion différentes (fixe et vectorielle) sont développés dans le cadre de la mobilité autonome.Le modèle et l’identification de la technologie de propulsion dite fixe sont basés sur un propulseur disponible commercialement. Le système de propulsion vectoriel est basé sur un prototype de propulseur magneto-couplé reconfigurable (PMCR) développé à l’IRDL-ENIB.Une méthode de commande non linéaire utilisant le modèle hydrodynamique de l’AUV est développée et son adaptation à deux systèmes de propulsion est présentée. Des analyses portant sur la commandabilité du robot et l’application de cette commande à différents systèmes sont proposées. L’optimisation globale est utilisée pour trouver des topologies propulsives et des paramètres de commande adaptés à la réalisation de tâches robotiques spécifiques. L’optimisation réalisée permet de trouver des solutions capables d’assurer le suivi de trajectoire et de minimiser la consommation énergétique du robot. L’optimisation utilise un algorithme génétique (algorithme évolutionnaire), une méthode d’optimisation stochastique appliquée ici à la conception orientée tâche de l’AUV. Les résultats de cette optimisation peuvent être utilisés comme une étape préliminaire dans la conception des AUVs, afin de donner des pistes pour améliorer les capacités de la propulsion.La technique d’optimisation est également appliquée au robot RSM (fabriqué au sein de l’IRDL-ENIB) en modifiant seulement quelques paramètres de sa topologie propulsive. Cela afin d’obtenir des configurations de propulsion adaptées au cours d’une seule et même mission aux spécificités locomotrices des tâches rencontrées : reconfiguration dynamique de la propulsion de l’AUV. / In this PhD thesis, the optimization of the propulsion and control of AUVs is developed. The hydrodynamic model of the AUVs is examined. Additionally, AUV propulsion topologies are studied and models for fixed and vectorial technology are developed. The fixed technology model is based on an off the shelf device, while the modeled vectorial propulsive system is based on a magnetic coupling thruster prototype developed in IRDL (Institut de Recherche Dupuy de Lôme) at ENI Brest. A control method using the hydrodynamic model is studied, its adaptation to two AUV topologies is presented and considerations about its applicability will be discussed. The optimization is used to find suitable propulsive topologies and control parameters in order to execute given robotic tasks, speeding up the convergence and minimizing the energy consumption. This is done using a genetic algorithm, which is a stochastic optimization method used for task-based design.The results of the optimization can be used as a preliminary stage in the design process of an AUV, giving ideas for enhanced propulsive configurations. The optimization technique is also applied to an IRDL existing robot, modifying only some of the propulsive topology parameters in order to readily adapt it to different tasks, making the AUV dynamically reconfigurable.

Distributed coordination of multi-agent systems (MASs) has been widely studied in various emerging engineering applications, including connected vehicles, wireless networks, smart grids, and cyber-physical systems. In these contexts, agents make the decision locally, relying on the interaction with their immediate neighbors over the connected communication networks. The study of distributed coordination for the multi-agent system (MAS) with constraints is significant yet challenging, especially in terms of ubiquitous uncertainties, the heavy communication burden, and communication delays, to name a few. Hence, it is desirable to develop distributed algorithms for the constrained MAS with these practical issues. In this dissertation, we develop the theoretical results on robust distributed model predictive control (DMPC) algorithms for two types of control problems (i.e., formation stabilization problem and consensus problem) of the constrained and uncertain MAS and apply robust DMPC algorithms in applications of cooperative marine vehicles. More precisely, Chapter 1 provides a systematic literature review, where the state-of-the-art DMPC for formation stabilization and consensus, robust MPC, and MPC for motion control of marine vehicles are introduced. Chapter 2 introduces some notations, necessary definitions, and some preliminaries. In Chapter 3, we study the formation stabilization problem of the nonlinear constrained MAS with un- certainties and bounded time-varying communication delays. We develop a min-max DMPC algorithm with the self-triggered mechanism, which significantly reduces the communication burden while ensuring closed-loop stability and robustness. Chapter 4 investigates the consensus problem of the general linear MAS with input constraints and bounded time-varying delays. We design a robust DMPC-based consensus protocol that integrates a predesigned consensus protocol with online DMPC optimization techniques. Under mild technical assumptions, the estimation errors propagated over prediction due to delay-induced inaccurate neighboring information are proved bounded, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying control input constraints. Chapter 5 proposes a Lyapunov-based DMPC approach for the formation tracking control problem of co-operative autonomous underwater vehicles (AUVs) subject to environmental disturbances. A stability constraint leveraging the extended state observer-based auxiliary control law and the associated Lyapunov function is incorporated into the optimization problem to enforce the stability and enhance formation tracking performance. A collision-avoidance cost is designed and employed in the DMPC optimization problem to further guarantee the safety of AUVs. Chapter 6 presents a tube-based DMPC approach for the platoon control problem of a group of heterogeneous autonomous surface vehicles (ASVs) with input constraints and disturbances. In particular, a coupled inter-vehicle safety constraint is added to the DMPC optimization problem; it ensures that neighboring ASVs maintain the safe distance and avoid inter-vehicle collision. Finally, we summarize the main results of this dissertation and discuss some potential directions for future research in Chapter 7. / Graduate / 2023-04-19

<p dir="ltr">The growing movement toward sustainable use of ocean resources is driven by the pressing need to alleviate environmental and human stressors on the planet and its oceans. From monitoring the food web to supporting sustainable fisheries and observing environmental shifts to protect against the effects of climate change, ocean observations significantly impact the Blue Economy. Acknowledging the critical role of Autonomous Underwater Vehicles (AUVs) in achieving persistent ocean exploration, this research addresses challenges focusing on the limited energy and storage capacity of AUVs, introducing a comprehensive underwater docking solution with a specific emphasis on enhancing the terminal homing phase through innovative vision algorithms leveraging neural networks.</p><p dir="ltr">The primary goal of this work is to establish a docking procedure that is failure-tolerant, scalable, and systematically validated across diverse environmental conditions. To fulfill this objective, a robust dock detection mechanism has been developed that ensures the resilience of the docking procedure through \comment{an} improved detection in different challenging environmental conditions. Additionally, the study addresses the prevalent issue of data sparsity in the marine domain by artificially generating data using CycleGAN and Artistic Style Transfer. These approaches effectively provide sufficient data for the docking detection algorithm, improving the localization of the docking station.</p><p dir="ltr">Furthermore, this work introduces methods to compress the learned docking detection model without compromising performance, enhancing the efficiency of the overall system. Alongside these advancements, a station-keeping algorithm is presented, enabling the mobile docking station to maintain position and heading while awaiting the arrival of the AUV. To leverage the sensors onboard and to take advantage of the computational resources to their fullest extent, this research has demonstrated the feasibility of simultaneously learning docking detection and marine wildlife classification through multi-task and transfer learning. This multifaceted approach not only tackles the limitations of AUVs' energy and storage capacity but also contributes to the robustness, scalability, and systematic validation of underwater docking procedures, aligning with the broader goals of sustainable ocean exploration and the blue economy.</p>