Numerical algorithms for efficiently solving optimal control problems are important for commonly used advanced control strategies, such as model predictive control (MPC), but can also be useful for advanced estimation techniques, such as moving horizon estimation (MHE). In MPC, the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem on-line, and in MHE the estimated states are obtained by solving an optimization problem that often can be formulated as a CFTOC problem. Common types of optimization methods for solving CFTOC problems are interior-point (IP) methods, sequential quadratic programming (SQP) methods and active-set (AS) methods. In these types of methods, the main computational effort is often the computation of the second-order search directions. This boils down to solving a sequence of systems of equations that correspond to unconstrained finite-time optimal control (UFTOC) problems. Hence, high-performing second-order methods for CFTOC problems rely on efficient numerical algorithms for solving UFTOC problems. Developing such algorithms is one of the main focuses in this thesis. When the solution to a CFTOC problem is computed using an AS type method, the aforementioned system of equations is only changed by a low-rank modification between two AS iterations. In this thesis, it is shown how to exploit these structured modifications while still exploiting structure in the UFTOC problem using the Riccati recursion. Furthermore, direct (non-iterative) parallel algorithms for computing the search directions in IP, SQP and AS methods are proposed in the thesis. These algorithms exploit, and retain, the sparse structure of the UFTOC problem such that no dense system of equations needs to be solved serially as in many other algorithms. The proposed algorithms can be applied recursively to obtain logarithmic computational complexity growth in the prediction horizon length. For the case with linear MPC problems, an alternative approach to solving the CFTOC problem on-line is to use multiparametric quadratic programming (mp-QP), where the corresponding CFTOC problem can be solved explicitly off-line. This is referred to as explicit MPC. One of the main limitations with mp-QP is the amount of memory that is required to store the parametric solution. In this thesis, an algorithm for decreasing the required amount of memory is proposed. The aim is to make mp-QP and explicit MPC more useful in practical applications, such as embedded systems with limited memory resources. The proposed algorithm exploits the structure from the QP problem in the parametric solution in order to reduce the memory footprint of general mp-QP solutions, and in particular, of explicit MPC solutions. The algorithm can be used directly in mp-QP solvers, or as a post-processing step to an existing solution. / Numeriska algoritmer för att effektivt lösa optimala styrningsproblem är en viktig komponent i avancerade regler- och estimeringsstrategier som exempelvis modellprediktiv reglering (eng. model predictive control (MPC)) och glidande horisont estimering (eng. moving horizon estimation (MHE)). MPC är en reglerstrategi som kan användas för att styra system med flera styrsignaler och/eller utsignaler samt ta hänsyn till exempelvis begränsningar i styrdon. Den grundläggande principen för MPC och MHE är att styrsignalen och de estimerade variablerna kan beräknas genom att lösa ett optimalt styrningsproblem. Detta optimeringsproblem måste lösas inom en kort tidsram varje gång som en styrsignal ska beräknas eller som variabler ska estimeras, och således är det viktigt att det finns effektiva algoritmer för att lösa denna typ av problem. Två vanliga sådana är inrepunkts-metoder (eng. interior-point (IP)) och aktivmängd-metoder (eng. active-set (AS)), där optimeringsproblemet löses genom att lösa ett antal enklare delproblem. Ett av huvudfokusen i denna avhandling är att beräkna lösningen till dessa delproblem på ett tidseffektivt sätt genom att utnyttja strukturen i delproblemen. Lösningen till ett delproblem beräknas genom att lösa ett linjärt ekvationssystem. Detta ekvationssystem kan man exempelvis lösa med generella metoder eller med så kallade Riccatirekursioner som utnyttjar strukturen i problemet. När man använder en AS-metod för att lösa MPC-problemet så görs endast små strukturerade ändringar av ekvationssystemet mellan varje delproblem, vilket inte har utnyttjats tidigare tillsammans med Riccatirekursionen. I denna avhandling presenteras ett sätt att utnyttja detta genom att bara göra små förändringar av Riccatirekursionen för att minska beräkningstiden för att lösa delproblemet. Idag har behovet av  parallella algoritmer för att lösa MPC och MHE problem ökat. Att algoritmerna är parallella innebär att beräkningar kan ske på olika delar av problemet samtidigt med syftet att minska den totala verkliga beräkningstiden för att lösa optimeringsproblemet. I denna avhandling presenteras parallella algoritmer som kan användas i både IP- och AS-metoder. Algoritmerna beräknar lösningen till delproblemen parallellt med ett förutbestämt antal steg, till skillnad från många andra parallella algoritmer där ett okänt (ofta stort) antal steg krävs. De parallella algoritmerna utnyttjar problemstrukturen för att lösa delproblemen effektivt, och en av dem har utvärderats på parallell hårdvara. Linjära MPC problem kan också lösas genom att utnyttja teori från multiparametrisk kvadratisk programmering (eng. multiparametric quadratic programming (mp-QP)) där den optimala lösningen beräknas i förhand och lagras i en tabell, vilket benämns explicit MPC. I detta fall behöver inte MPC problemet lösas varje gång en styrsignal beräknas, utan istället kan den förberäknade optimala styrsignalen slås upp. En nackdel med mp-QP är att det krävs mycket plats i minnet för att spara lösningen. I denna avhandling presenteras en strukturutnyttjande algoritm som kan minska behovet av minne för att spara lösningen, vilket kan öka det praktiska användningsområdet för mp-QP och explicit MPC.

Increasing worldwide demand for petroleum motivates greater efficiency, safety, and environmental responsibility in upstream oil and gas processes. The objective of this research is to improve these areas with advanced control methods. This work develops the integration of optimal control methods including model predictive control, moving horizon estimation, high fidelity simulators, and switched control techniques applied to subsea riser slugging and managed pressure drilling. A subsea riser slugging model predictive controller eliminates persistent offset and decreases settling time by 5% compared to a traditional PID controller. A sensitivity analysis shows the effect of riser base pressure sensor location on controller response. A review of current crude oil pipeline wax deposition prevention, monitoring, and remediation techniques is given. Also, industrially relevant control model parameter estimation techniques are reviewed and heuristics are developed for gain and time constant estimates for single input/single output systems. The analysis indicates that overestimated controller gain and underestimated controller time constant leads to better controller performance under model parameter uncertainty. An online method for giving statistical significance to control model parameter estimates is presented. Additionally, basic and advanced switched model predictive control schemes are presented. Both algorithms use control models of varying fidelity: a high fidelity process model, a reduced order nonlinear model, and a linear empirical model. The basic switched structure introduces a method for bumpless switching between control models in a predetermined switching order. The advanced switched controller builds on the basic controller; however, instead of a predetermined switching sequence, the advanced algorithm uses the linear empirical controller when possible. When controller performance becomes unacceptable, the algorithm implements the low order model to control the process while the high fidelity model generates simulated data which is used to estimate the empirical model parameters. Once this online model identification process is complete, the controller reinstates the empirical model to control the process. This control framework allows the more accurate, yet computationally expensive, predictive capabilities of the high fidelity simulator to be incorporated into the locally accurate linear empirical model while still maintaining convergence guarantees.

Recently, autonomous vehicles have received worldwide attentions from academic research, automotive industry and the general public. In order to achieve a higher level of automation, one of the most fundamental requirements of autonomous vehicles is the capability to respond to internal and external changes in a safe, timely and appropriate manner. Situational awareness and decision making are two crucial enabling technologies for safe operation of autonomous vehicles. This thesis presents a solution for improving the automation level of autonomous vehicles in both situational awareness and decision making aspects by utilising additional domain knowledge such as constraints and influence on a moving object caused by environment and interaction between different moving objects. This includes two specific sub-systems, model based target tracking in environmental perception module and motion planning in path planning module. In the first part, a rigorous Bayesian framework is developed for pooling road constraint information and sensor measurement data of a ground vehicle to provide better situational awareness. Consequently, a new multiple targets tracking (MTT) strategy is proposed for solving target tracking problems with nonlinear dynamic systems and additional state constraints. Besides road constraint information, a vehicle movement is generally affected by its surrounding environment known as interaction information. A novel dynamic modelling approach is then proposed by considering the interaction information as virtual force which is constructed by involving the target state, desired dynamics and interaction information. The proposed modelling approach is then accommodated in the proposed MTT strategy for incorporating different types of domain knowledge in a comprehensive manner. In the second part, a new path planning strategy for autonomous vehicles operating in partially known dynamic environment is suggested. The proposed MTT technique is utilized to provide accurate on-board tracking information with associated level of uncertainty. Based on the tracking information, a path planning strategy is developed to generate collision free paths by not only predicting the future states of the moving objects but also taking into account the propagation of the associated estimation uncertainty within a given horizon. To cope with a dynamic and uncertain road environment, the strategy is implemented in a receding horizon fashion.

Micro aerial vehicles and other autonomous systems have the potential to truly transform life as we know it, however much of the potential of autonomous systems remains unrealized because reliable navigation is still an unsolved problem with significant challenges. This dissertation presents solutions to many aspects of autonomous navigation. First, it presents ROSflight, a software and hardware architure that allows for rapid prototyping and experimentation of autonomy algorithms on MAVs with lightweight, efficient flight control. Next, this dissertation presents improvments to the state-of-the-art in optimal control of quadrotors by utilizing the error-state formulation frequently utilized in state estimation. It is shown that performing optimal control directly over the error-state results in a vastly more computationally efficient system than competing methods while also dealing with the non-vector rotation components of the state in a principled way. In addition, real-time robust flight planning is considered with a method to navigate cluttered, potentially unknown scenarios with real-time obstacle avoidance. Robust state estimation is a critical component to reliable operation, and this dissertation focuses on improving the robustness of visual-inertial state estimation in a filtering framework by extending the state-of-the-art to include better modeling and sensor fusion. Further, this dissertation takes concepts from the visual-inertial estimation community and applies it to tightly-coupled GNSS, visual-inertial state estimation. This method is shown to demonstrate significantly more reliable state estimation than visual-inertial or GNSS-inertial state estimation alone in a hardware experiment through a GNSS-GNSS denied transition flying under a building and back out into open sky. Finally, this dissertation explores a novel method to combine measurements from multiple agents into a coherent map. Traditional approaches to this problem attempt to solve for the position of multiple agents at specific times in their trajectories. This dissertation instead attempts to solve this problem in a relative context, resulting in a much more robust approach that is able to handle much greater intial error than traditional approaches.

D'une manière schématique, l'optimisation dynamique de procédés consiste en trois étapes de base : (i) la modélisation, dans laquelle un modèle (phénoménologique) du procédé est construit, (ii) la formulation du problème, dans laquelle le critère de performance, les contraintes et les variables de décision sont définis, (iii) et la résolution, dans laquelle les profils optimaux des variables de décision sont déterminés. Il est important de souligner que ces profils optimaux garantissent l'optimalité pour le modèle mathématique utilisé. Lorsqu'ils sont appliqués au procédé, ces profils ne sont optimaux que lorsque le modèle décrit parfaitement le comportement du procédé, ce qui est très rarement le cas dans la pratique. En effet, les incertitudes sur les paramètres du modèle, les perturbations du procédé, et les erreurs structurelles du modèle font que les profils optimaux des variables de décision basés sur le modèle ne seront probablement pas optimaux pour le procédé. L'application de ces profils au procédé conduit généralement à la violation de certaines contraintes et/ou à des performances sous-optimales. Pour faire face à ces problèmes, l'optimisation dynamique en temps-réel constitue une approche tout à fait intéressante. L'idée générale de cette approche est d'utiliser les mesures expérimentales associées au modèle du procédé pour améliorer les profils des variables de décision de sorte que les conditions d'optimalité soient vérifiées sur le procédé (maximisation des performances et satisfaction des contraintes). En effet, pour un problème d'optimisation sous contraintes, les conditions d'optimalité possèdent deux parties : la faisabilité et la sensibilité. Ces deux parties nécessitent différents types de mesures expérimentales, à savoir les valeurs du critère et des contraintes, et les gradients du critère et des contraintes par rapport aux variables de décision. L'objectif de cette thèse est de développer une stratégie conceptuelle d'utilisation de ces mesures expérimentales en ligne de sorte que le procédé vérifie non seulement les conditions nécessaires, mais également les conditions suffisantes d'optimalité. Ce développement conceptuel va notamment s'appuyer sur les récents progrès en optimisation déterministe (les méthodes stochastiques ne seront pas abordées dans ce travail) de procédés basés principalement sur l'estimation des variables d'état non mesurées à l'aide d'un observateur à horizon glissant. Une méthodologie d'optimisation dynamique en temps réel (D-RTO) a été développée et appliquée à un réacteur batch dans lequel une réaction de polymérisation par greffage a lieu. L'objectif est de déterminer le profil temporel de température du réacteur qui minimise le temps opératoire tout en respectant des contraintes terminales sur le taux de conversion et l'efficacité de greffage / In a schematic way, process optimization consists of three basic steps: (i) modeling, in which a (phenomenological) model of the process is developed, (ii) problem formulation, in which the criterion of Performance, constraints and decision variables are defined, (iii) the resolution of the optimal problem, in which the optimal profiles of the decision variables are determined. It is important to emphasize that these optimal profiles guarantee the optimality for the model used. When applied to the process, these profiles are optimal only when the model perfectly describes the behavior of the process, which is very rarely the case in practice. Indeed, uncertainties about model parameters, process disturbances, and structural model errors mean that the optimal profiles of the model-based decision variables will probably not be optimal for the process. The objective of this thesis is to develop a conceptual strategy for using experimental measurements online so that the process not only satisfies the necessary conditions, but also the optimal conditions. This conceptual development will in particular be based on recent advances in deterministic optimization (the stochastic methods will not be dealt with in this work) of processes based on the estimation of the state variables that are not measured by a moving horizon observer. A dynamic real-time optimization (D-RTO) methodology has been developed and applied to a batch reactor where polymer grafting reactions take place. The objective is to determine the on-line reactor temperature profile that minimizes the batch time while meeting terminal constraints on the overall conversion rate and grafting efficiency

L’estimation de trajectoires de débris spatiaux pendant la rentrée atmosphérique est un défi majeur pour les prochaines années, renforcé par plusieurs projets liés à l'enlèvement de débris établis par plusieurs agences spatiales. Cependant, ce problème s’avère complexe du fait des erreurs de modèle et des difficultés d’initialisation des algorithmes d’estimation induites par une mauvaise connaissance de la dynamique des débris suite à leur désintégration pendant la phase de rentrée atmosphérique. Tout estimateur choisi doit donc être robuste vis-à-vis de ces facteurs. L’estimateur à horizon glissant (MHE) est reconnu dans la littérature pour être robuste vis-à-vis d’erreurs de modèle et de mauvaise initialisation, et les travaux de thèse ont montré qu’il était adapté en termes de performances à la problématique de l’estimation des débris en phase de rentrée. En revanche, il se fonde sur une stratégie d’optimisation qui requiert de fait un temps de calcul important. Pour pallier ce problème, une nouvelle structure d’estimation à horizon glissant a été développée, impliquant un temps de calcul faible nécessaire à l’application envisagée. Cette stratégie, appelée « estimateur à horizon glissant avec pré-estimation (MHE-PE)», prend en compte les erreurs de modèle via un estimateur auxiliaire, plutôt que de chercher à obtenir les estimées du bruit d’état sur l’horizon d’estimation, comme le fait la structure de l’estimateur MHE standard. Un théorème garantissant la stabilité de la dynamique de l’erreur d’estimation du MHE-PE a par ailleurs été proposé. Enfin, les performances de cette structure dans le cadre de l’estimation en trois dimensions des trajectoires de débris pendant la phase de rentrée se sont avérées meilleures que celles observées avec des estimateurs classiques. En particulier, sans dégrader la précision et la convergence de l’estimation, l’estimateur MHE-PE requiert moins de temps de calcul du fait du nombre réduit de paramètres à optimiser. / Space debris tracking during atmospheric re-entries will be a crucial challenge in the coming years, emphasized through many projects on space debris mitigation established by space agencies worldwide. However, this problem appears to be complex, due to model errors and difficulties to properly initialize the estimation algorithms, as a result of unknown dynamics of the debris and their disintegrations during the re-entries. A-to-be used estimator for this problem must be robust against these factors. The Moving Horizon Estimator (MHE) is known in the literature to be robust to model errors and bad initialization, and the PhD work has proved its ability to satisfy performances required by the debris tracking during the re-entries. However, its optimization-based framework induces a large computation time. To overcome this, a new MHE structure which requires smaller computation time than the classical MHE has been developed. This strategy, so-called “Moving Horizon Estimator with Pre-Estimation (MHE-PE)” takes into account model errors by using an auxiliary estimator rather than by searching for estimates of the process noise sequence over the horizon as in the classical strategy. A theorem which guarantees the stability of the dynamics of the estimation errors of the MHE-PE has also been proposed. Finally, performances of this structure in the context of 3D space debris tracking during the re-entries have been shown to be better than those obtained with classical estimators including the MHE. In particular, without degrading accuracy of the estimates and convergence of the estimator, the MHE-PE estimator requires smaller computation time than the MHE thanks to its small number of optimization variables.

Cette thèse concerne principalement la résolution des problèmes d’inversion dynamiquedans le cadre des systèmes dynamiques non linéaires. Ainsi, un ensemble de techniquesbasées sur l’utilisation des trains de mesures passées et sauvegardées sur une fenêtreglissante, a été développé. En premier lieu, les mesures sont utilisées pour générerune famille de signatures graphiques, qui constituent un outil de classification permettantde discriminer les diverses valeurs des variables à estimer pour un système non linéairedonné. Cette première technique a été appliquée à la résolution de deux problèmes : leproblème d’électolocation d’un robot doté du sens électrique et le problème d’estimationd’état dans les systèmes à dynamiques non linéaires. Outre ces deux applications, destechniques d’inversion à horizon glissant spécifiques au problème de diagnostic des défautsd’éoliennes dans le cadre d’un benchmark international ont été développées. Cestechniques sont basées sur la minimisation de critères quadratiques basés sur des modèlesde connaissance. / This thesis mainly concerns the resolution of dynamic inverse problems involvingnonlinear dynamical systems. A set of techniques based on the use of trains of pastmeasurements saved on a sliding window was developed. First, the measurements areused to generate a family of graphical signatures, which is a classification tool, in orderto discriminate between different values of variables to be estimated for a given nonlinearsystem. This technique was applied to solve two problems : the electrolocationproblem of a robot with electrical sense and the problem of state estimation in nonlineardynamical systems. Besides these two applications, receding horizon inversion techniquesdedicated to the fault diagnosis problem of a wind turbine proposed as an internationalbenchmark were developed. These techniques are based on the minimization of quadraticcriteria based on knowledge-based models.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The data reconciliation in process control is extremely important regarding the industries because from this it is possible to obtain a greater efficiency in the performance in industrial process control meshes aiming at a lower cost and a higher quality of the product. In this work we approach data estimation techniques for the implementation of an online dynamic data reconciliation system in order to reduce the noise and the measurement uncertainties that are submitted in the process variables. The techniques used here are: the Kalman Filter, the Preditor-Corrector DDR Algorithm, the Moving Horizon Estimator (MHE) and the Constrained Extended Kalman Filter (CEKF). The analysis is performed by applying the dynamic data reconciliation system in a simulated process, characteristic of the chemical industry, operating under MPC (Model Predictive Control). The performance of the MPC controller is also enhanced by the use of the reconciled data in the feedback control loop. / A reconciliação de dados em controle de processos é extremamente importante no que diz respeito às indústrias, pois a partir dessa é possível obter uma maior eficiência no desempenho em malhas de controle de processos industriais visando à minimização dos custos e maximizando a qualidade do produto. Neste trabalho abordam-se técnicas de estimação de dados para a implementação de um sistema de reconciliação dinâmica de dados on-line a fim de reduzir os ruídos e as incertezas de medições a que estão submetidas às variáveis do processo. As técnicas aqui empregadas são: o Filtro de Kalman, o Algoritmo DDR Preditor-Corretor, o Estimador de Horizonte Móvel (MHE) e o Filtro de Kalman Estendido com Restrições (CEKF). As análises são efetuadas aplicando o sistema de reconciliação dinâmica de dados em um processo simulado, característico da indústria química, operando sob controle preditivo (MPC). Também é efetuado o aprimoramento no desempenho do controlador MPC utilizando os dados reconciliados na malha de realimentação do controlador.

[pt] A crescente demanda por sistemas de transporte autônomos e inteligentes exige o desenvolvimento de técnicas avançadas de controle e estimativa, visando garantir operações seguras e eficientes. Devido à natureza não linear da dinâmica veicular e seus fenômenos característicos, os métodos clássicos de estimativa e controle podem não alcançar resultados adequados, o que incentiva a pesquisa de novos algoritmos. Por algumas contribuições, a primeira parte deste trabalho trata de algoritmos de estimação, tanto para identificação de parâmetros invariantes no tempo, quanto para estimação de estados e parâmetros variantes no tempo. Especial destaque é dados aos algoritmos de Estimação de Estados por Horizonte Móvel (MHSE), que se apresenta como robusto e preciso, devido ao problema de otimização com restrição em que se baseia. Este algoritmo é avaliado em dinâmica longitudinal de veículos, para estimativa de deslizamento longitudinal e coeficiente de atrito pneu-estrada. Apesar de sua eficiência, o alto custo computacional torna necessária a busca por alternativas sub-ótimas, e o emprego de Redes Neurais que mapeiam os resultados da otimização é uma solução promissora, que é tratada como Estimação por Horizonte Móvel com Redes Neurais (NNMHE). O NNMHE é avaliado em uma estimativa do estado de carga (SOC) de baterias para veículos elétricos, demonstrando, através de dados experimentais, que o NNMHE emula com precisão o problema de otimização e a literatura indica sua aplicação efetiva em hardwares embarcados. Por fim, é apresentada uma contribuição sobre o controle preditivo baseado em modelo não linear (NMPC). É proposto e avaliado seu uso compondo uma nova estrutura de controle hierárquica para veículos elétricos com motores independentes nas rodas, através do qual é possível controlar adequadamente o veículo em tarefas de rastreamento de velocidade e trajetória, com reduzido esforço computacional. O controle é avaliado usando dados experimentais de pneus obtidos, que aproximam a simulação de situações reais. / [en] The rising demand of autonomous and intelligent transportation systems requires the development of advanced control and estimation techniques, aiming to ensure safety and efficient operations. Due to the nonlinear nature of vehicle dynamics and its characteristic phenomena, classical estimation and control methods may not achieve adequate results, which encourages the research of novel algorithms. By some contributions, the first part of this work deals with estimation algorithms, both for identification of time invariant parameters and for estimation of states and time varying parameters. Special emphasis is given to Moving-Horizon State Estimation (MHSE), which is presented to be robust and accurate, due to the constrained optimization problem on which it is based. This algorithm is evaluated in vehicle longitudinal dynamics, for slip and tire-road friction estimation. Despite its efficiency, the high computational cost makes it necessary to search for suboptimal alternatives, and the employ of a Neural Networks that maps the optimization results is a promising solution, which is treated as Neural Networks Moving-Horizon Estimation (NNMHE). The NNMHE is evaluated on a state-of-charge (SOC) estimation of batteries for electric vehicles, demonstrating, through experimental data, that the NNMHE emulates accurately the optimization problem, and the literature indicates its effectively application on embedded hardware. Finally, a contribution about Nonlinear Model-based Predictive Control (NMPC) is presented. It is proposed and evaluated its use compounding a novel hierarchical control framework for electric vehicles with independent in-wheel motors, through which it is possible to adequately control the vehicle on velocity and path tracking tasks, with reduced computational effort. The control is evaluated using experimental obtained tire data, which approaches the simulation to real situations.

Cette thèse contribue à répondre au problème de la gestion optimale des ressources en eau dans les réseaux de navigation intérieure du point de vue de la théorie du contrôle. Les objectifs principales à atteindre consistent à garantir la navigabilité des réseaux de voies navigables, veiller à la réduction des coûts opérationnels et à la longue durée de vie des équipements. Lors de la conception de lois de contrôle, les caractéristiques des réseaux doivent être prises en compte, à savoir leurs dynamiques complexes, des retards variables et l’absence de pente. Afin de réaliser la gestion optimale, le contrôle efficace des structures hydrauliques doit être assuré. A cette fin, une approche de modélisation orientée contrôle est dérivée. Cependant, la formulation obtenue appartient à la classe des systèmes de descripteurs retardés, pour lesquels la commande prédictive MPC et l’estimation d’état sur horizon glissant MHE peuvent être facilement adaptés à cette formulation, tout en permettant de gérer les contraintes physiques et opérationnelles de manière naturelle. En raison de leur grande dimensionnalité, une mise en œuvre centralisée n’est souvent ni possible ni souhaitable. Compte tenu du fait que les réseaux de navigation intérieure sont des systèmes fortement couplés, une approche distribuée est proposée, incluant un protocole de communication entre agents. Malgré l’optimalité des solutions, toute erreur peut entraîner une gestion inefficace du système. Par conséquent, les dernières contributions de la thèse concernent la conception de stratégies de supervision permettant de détecter et d’isoler les pannes des équipements. Toutes les approches présentées sont appliquées à une étude de cas réaliste basée sur le réseau de voies navigables du nord e la France afin de valider leur efficacité. / This thesis addresses the problem of optimal management of water resources in inland navigation networks from a control theory perspective. The main objectives to be attained consist in guaranteeing the navigability condition of the network, minimizing the operational cost and ensuring a long lifespan of the equipment. However, their complex dynamics, large time delays and negligible bottom slopes complicate their management. In order to achieve the optimal management, the efficient control of the hydraulic structures must be ensured. To this end, a control-oriented modeling approach is derived. The resulting formulation belongs to the class of delayed desciptor systems, for which model predictive control and moving horizon estimation can be easily adapted, as well as being able to deal with physical and operational constraints in a natural manner. However, a centralized implementation is often neither possible nor desirable. As these networks are strongly coupled systems, a distributed approach is followed, featuring a communication protocol among agents. Despite the optimality of the solutions, any malfunction can lead to an inefficient system management. Therefore, the last part of the thesis regards the design of supervisory strategies that allow to detect and isolate faults. All the presented approaches are applied to a realistic case study based on the inland navigation network in the north of France to validate their effectiveness.