Buffer tanks are widely used within the process industry to prevent flow variations from being directly propagated throughout a plant. The capacity of the tank is used to smoothly transfer inlet flow upsets to the outlet. Ideally, the tank thus works as a low pass filter where the available tank capacity limits the achievable flow smoothing. For infrequently occurring upsets, where the system has time to reach steady state between flow changes, the averaging level control problem has been extensively studied. After an inlet flow change, flow filtering has traditionally been obtained by letting the tank level deviate from its nominal value while slowly adapting the outlet to cancel out the flow imbalance and eventually bringing back the level to its set-point. The system is then again in steady state and ready to surge the next upset. By ensuring that the single largest upset can be handled without violating the level constraints, satisfactory flow smoothing is obtained. In this thesis, the smoothing of frequently changing inlet flows is addressed. In this case, standard level controllers struggle to obtain acceptable flow smoothing since the system rarely is in steady state and flow upsets can thus not be treated as separate events. To obtain a control law that achieves optimal filtering while directly accounting for future upsets, the averaging level control problem was approached using robust model predictive control (MPC). The robust MPC differs in the way it obtains flow smoothing by not returning the tank level to a fixed set-point. Instead, it lets the steady state tank level depend on the current value of the inlet flow. This insight was then used to propose a linear control structure, designed to filter frequent upsets optimally. Analyses and simulation results indicate that the proposed linear and robust MPC controller obtain flow smoothing comparable to the standard optimal averaging level controllers for infrequent upsets while handling frequent upsets considerably better.

De nos jours, les éoliennes contribuent à une large partie de production d'énergie dans le monde. En 2013, 2,7% de la production d'électricité mondiale était éolienne, avec un objectif d'atteindre 14% de la demande d'électricité totale en 2020. Pour satisfaire ces exigences, la taille standard de la turbine éolienne tend à grandir. Les éoliennes de tailles des mégawatts sont très coûteuses, et leur rendement devrait être optimisé pour maximiser l'énergie produite et protéger les équipements de toute dégradation pour optimiser leur durée de vie.Dans ce projet de thèse, la commande prédictive à base de modèle (MPC) est utilisée pour la commande et la commande tolérante aux défauts de l'éolienne. Afin d'optimiser le temps de calcul de la commande MPC, qui peut rendre son implémentation en ligne irréalisable, les entrées de la commande ont été paramétrées par les fonctions de Laguerre (LMPC) ou les fonctions de Kautz (KMPC). Ceci a permis de réduire le temps de calcul d'un tiers. La commande MPC robuste par approche min-max a également été considérée dans l'objectif de rendre la stratégie de commande robuste aux incertitudes paramétriques, et à l'apparition de défauts. Ces différentes stratégies ont état évaluées sur un modèle de l'éolienne à deux masses, avec une commande multi entrée/multi avec contraintes sur les entrées et les sorties.Dans le chapitre V, la commande MPC paramétrée par les fonctions de Laguerre ou de Kautz a été reformulée dans l'unique objectif de compenser le défaut. En effet, sur une éolienne en fonctionnement stable et possédant des lois de commande qui ne s'accommode pas aux défauts, il est possible de calculer la correction nécessaire à considérer par les lois existantes afin de compenser le défaut, si le défaut est bien détecté et estimé. Cette stratégie est recherchée si l'industriel ne souhaite pas changer les lois de commande établies sur l'éolienne, car les stratégies de commande MPC discutées peuvent faire l'ensemble de travail : poursuite de la trajectoire désirée et l'accommodation aux défauts / Nowadays, wind turbines contribute to a large part of energy production in the world. In 2013, 2.7% of global electricity production was based on wind power, with a goal of reaching 14% of total electricity demand in 2020. The progression was remarkable in the last years, namely in France where the wind power generation increased from 2.5 TWh (terawatt-hour) in 2013 to 21.1 TWh in 2015.In order to satisfy these objectives, the standard size of the wind turbine tends to grow. However, the megawatt size wind turbines are very expensive and thus their efficiency has to be optimized in order to maximize the produced energy. Furthermore, it is aimed to protect the equipment from damage and maximize the service life of wind turbines, which is usually 20 years.In this thesis, model predictive control (MPC) is used to control the wind turbine and to identify the faults that could occur. Since the computation time in the MPC strategy is high, its use in real time fast systems may become unfeasable. To overcome this difficulty, the MPC control inputs are parametrized by Laguerre functions (LMPC) or Kautz functions (KMPC). This allowed decreasing the computation time by 33% compared to non-parametrized MPC. The min-max MPC approach is also considered in order to render the control strategy robust to parametric uncertainties and faults scenarios.These control strategies are evaluated on a wind turbine model with a multi-input (pitch angle and generator torque) / multi- output (generator power and generator speed) control, with constraints on inputs and outputs. These results are discussed in Chapter IV.In Chapter V, the Laguerre or Kautz parameterized MPC is reformulated with the objective of faults compensations. Indeed, if the faults are detected and estimated then it is possible to calculate the correction required to compensate these faults. This strategy becomes interesting from a wind turbine is operated with a controller that is not aimed to be changed for security or cost reasons, and the objective of the operator is only to compensate actuator or sensor faults. In these simulations, an available benchmark was used with the controller implemented in it.The thesis also contains a bibliographic and three introductory chapters discussing the state of the art of the turbine model, its control, fault detection and the MPC strategies used in this work

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