Cette thèse est dediée à l'alanyse numérique de méthodes numériques dans le domaine du contrôle optimal, et est composée de deux parties. La première partie est consacrée à des nouveaux résultats concernant des méthodes numériques pour le contrôle optimal de systèmes hybrides, qui peuvent être contrôlés simultanément par des fonctions mesurables et des sauts discontinus dans la variable d'état. La deuxième partie est dédiée è l'étude d'une application spécifique surl'optimisation de trajectoires pour des lanceurs spatiaux avec contraintes en probabilité. Ici, on utilise des méthodes d'optimisation nonlineaires couplées avec des techniques de statistique non parametrique. Le problème traité dans cette partie appartient à la famille des problèmes d'optimisation stochastique et il comporte la minimisation d'une fonction de coût en présence d'une contrainte qui doit être satisfaite dans les limites d'un seuil de probabilité souhaité. / This thesis is devoted to the analysis of numerical methods in the field of optimal control, and it is composed of two parts. The first part is dedicated to new results on the subject of numerical methods for the optimal control of hybrid systems, controlled by measurable functions and discontinuous jumps in the state variable simultaneously. The second part focuses on a particular application of trajectory optimization problems for space launchers. Here we use some nonlinear optimization methods combined with non-parametric statistics techniques. This kind of problems belongs to the family of stochastic optimization problems and it features the minimization of a cost function in the presence of a constraint which needs to be satisfied within a desired probability threshold.

Le logiciel embarqué est devenuaujourd’hui incontournable dans la plupart dessecteurs industriels. Ce dernier fait appel engénéral à des connaissances métier différentes.L’ensemble du système (le logiciel et sonenvironnement) est ainsi spécifié d’une manièrehétérogène, avec des parties discrètes et d’autrescontinues. La simulation de ces systèmeshybrides nécessite des données précises et unesynchronisation des changements continus avecles transitions discrètes. Mais, dans les premièresphases de conception, l’absence des informationsempêche de simuler le système numériquement.Dans notre thèse, nous présentons un nouveaulangage qualitatif dédié à la simulationqualitative des systèmes hybrides. Ce nouveaulangage consiste à modéliser les relations entreles variables du système. Il est implémenté dansDiversity, un moteur d’exécution symbolique,pour construire les traces du système. Nousavons appliqué cette approche à l’analyse desmodèles SysML, en utilisant une transformationM2M à partir de SysML vers un langage pivot,une transformation M2T à partir de ce langagevers Diversity. Nous avons aussi analysé lestraces brutes de l’exécution symbolique deDiversity pour construire les comportementsqualitatifs du système. / Embedded software has becomeessential in most industrial sectors. The latterusually involves various business knowledge.The whole system (the software and itsenvironment) is specified in a heterogeneousform, with discrete and continuous parts.Simulating these hybrid systems requiresprecise data and synchronization of continuouschanges and discrete transitions. However, inthe first design steps, missing informationforbids numerical simulation. We present in ourthesis a new qualitative language for qualitativesimulation of hybrid systems, which consists incomputing the relationships between the systemvariables. This language is implemented in theDiversity symbolic execution engine to build thetraces of the system. We apply this approach tothe analysis of SysML models, using an M2Mtransformation from SysML to a pivot language,an M2T transformation from this language toDiversity. We also analyze the brutal symbolictraces obtained by Diversity to build the realqualitative behaviors of the system.

Dans cette thèse, nous analysons la dynamique de systèmes à événements discrets avec synchronisation et priorités, au moyen de réseaux de Petri et de réseaux de files d'attente.Nous appliquons cela à l'évaluation de performance d'un centre d'appels d'urgence.Notre motivation de départ est pratique. Pendant la durée de ce travail, un nouveau centre d'appels d'urgence a été mis en place pour l'agglomération parisienne, traitant les appels pour la police et les pompiers.La nouvelle organisation traite les appels en deux niveaux.Un premier niveau d'opérateurs répond aux appels, identifie les appels urgents et traite les appels non urgents.Les opérateurs de second niveau sont spécialistes (policiers ou pompiers) et traitent les demandes d'intervention.Quand un appel est identifié au niveau 1 comme très urgent, l'opérateur reste en ligne avec l'appelant jusqu'à ce qu'un opérateur de niveau 2 réponde. De plus, l'appel est prioritaire.Une conséquence de cette procédure est que, lorsqu'aucun opérateur de niveau 2 n'est disponible, les opérateurs de niveau 1 attendent avec ces appels très urgents, et la capacité du niveau 1 diminue.Nous nous intéressons à l'évaluation de performance de divers systèmes correspondant à cette description générale, dans des situations de saturation.Nous proposons trois modèles différents pour traiter ce type de systèmes.Les deux premiers sont des modèles de réseaux de Petri temporisés.Nous enrichissons les classiques réseaux de Petri à choix libres en autorisant des situations de conflit où le routage est résolu par des priorités.La principale difficulté est alors que l'opérateur de la dynamique n'est plus monotone.Dans un premier modèle, nous proposons une dynamique discrète pour cette classe de réseaux de Petri, avec des temps de séjour constants sur les places.Nous prouvons que les variables compteurs d'une exécution du réseau sont les solutions d'un système affine par morceaux, avec retards.Nous étudions les régimes stationnaires de cette dynamique, et caractérisons les régimes affines comme solutoins d'un système affine par morceaux, qui peut être vu comme un système sur le semi-corps de germes tropical (min plus).Les applications numériques montrent cependant que la convergence ne se fait pas toujours vers ces régimes stationnaires affines.Le second modèle est une transformation continue du précédent. Pour la même classe de réseaux de Petri, nous proposons une dynamique sous forme d'équations différentielles discontinues.Nous établissons l'existence et l'unicité de la solution.L'objectif de cette modélisation est d'obtenir un système plus simple dans lequel les pathologies du temps discret disparaissent. Nous montrons que les régimes stationaires sont les mêmes que ceux de la dynamique discrète. Les simulations numériques semblent montrer que la convergence s'obtient effectivement dans ce cas.Nous modélisons aussi le centre d'appels d'urgence comme un réseau de files d'attente, prenant ainsi en compte le caractère aléatoire des différentes variables du centre d'appel.Pour ce système, nous prouvons que la dynamique, après une transformation d'échelle, converge vers une limite fluide, qui correspond au système d'équations différentielles précédent.Cela conforte notre seconde modélisation.Les principaux outils de la preuve de convergence sont le calcul stochastique pour les processus de Poisson, les formulations de Skorokhod généralisées, ou encore des arguments de couplage.Ainsi, nos trois modèles d'un même centre d'appels d'urgence définissent un même comportement asymptotique schématique, décrivant différentes phases de congestion du centre.Dans une seconde partie de cette thèse, nous analysons des simulations poussées, prenant en compte les nombreux détails de notre étude de cas. Les simulations confirment le comportement schématique prédit par nos modèles mathématiques. Nous discutons aussi des interactions complexes provenant de la nature hétérogène du niveau 2. / In this thesis, we analyze the dynamics of discrete event systems with synchronization and priorities, by the means of Petri nets and queueing networks.We apply this to the performance evaluation of an emergency call center.Our original motivation is practical. During the period of this work, a new emergency call center became operative in Paris area, handling emergency calls to police and firemen.The new organization includes a two-level call treatment. A first level of operators answers calls, identifies urgent calls and handles (numerous) non-urgent calls.Second level operators are specialists (policemen or firemen) and handle emergency demands.When a call is identified at level 1 as extremely urgent, the operator stays in line with the call until a level 2 operator answers. The call has priority for level 2 operators.A consequence of this procedure is that, when level 2 operators are busy, level 1 operators wait with extremely urgent calls, and the capacity of level 1 diminishes.We are interested in the performance evaluation of various systems corresponding to this general description, in stressed situations.We propose three different models addressing this kind of systems.The first two are timed Petri net models.We enrich the classical free choice Petri nets by allowing conflict situations in which the routing is solved by priorities.The main difficulty in this situation is that the operator of the dynamics becomes non monotone.In a first model, we consider discrete dynamics for this class of Petri nets, with constant holding times on places.We prove that the counter variables of an execution of the Petri net are solutions of a piecewise linear system with delays.As far as we know, this proof is new, even for the class of free choice nets, which is a subclass of ours.We investigate the stationary regimes of the dynamics, and characterize the affine ones as solutions of a piecewise linear system, which can be seen as a system over a tropical (min-plus) semifield of germs.Numerical experiments show that, however, convergence does not always holds towards these affine stationary regimes.The second model is a ``continuization'' of the previous one. For the same class of Petri nets, we propose dynamics expressed by differential equations, so that the tokens and time events become continue.For this differential system with discontinuous righthandside, we establish the existence and uniqueness of the solution.By using differential equations, we aim at obtaining a simpler model in which discrete time pathologies disappear. We show that the stationary regimes are the same as the stationary regimes of the discrete time dynamics.Numerical experiments tend to show that, in this setting, convergence effectively holds.We also model the emergency call center described above as a queueing system, taking into account the randomness of the different call center variables.For this system, we prove that, under an appropriate scaling, the dynamics converges to a fluid limit which corresponds to the differential equations of our Petri net model.This provides support for the second model.Stochastic calculus for Poisson processes, generalized Skorokhod formulations and coupling arguments are the main tools used to establish this convergence.Hence, our three models of an identical emergency call center yield the same schematic asymptotic behavior, expressed as a piecewise linear system of the parameters, and describing the different congestion phases of the system.In a second part of this thesis, simulations are carried out and analyzed, taking into account the many subtleties of our case study (for example, we construct probability distributions based on real data analysis).The simulations confirm the schematic behavior described by our mathematical models.We also address the complex interactions coming from the heterogeneous nature of level 2.

No description available.

<p>Cyber-physical systems (CPS) are a class of complicated systems integrating cyber components with physical components. Although such a cyber-physical interaction improves the system performance and intelligence, it increases the system complexity and makes the system vulnerable to various types of faults, failures, and cyber-attacks. To assure the security and improve the resiliency of CPS, it is found that the hybrid system model can be a powerful tool in the domain of fault detection and isolation, cyber-attack diagnosis and containment, as well as resilient control and reconfiguration. Several problems are concerned in this dissertation. For situational awareness, \textit{mode discernibility}, which stands for whether the discrete state of a hybrid system can be correctly identified, is characterized and discussed with potential applications to monitoring system design. For CPS vulnerability analysis, the problem of stealthy attack design for systems with switching structures is investigated, which is motivated by the recent literature. To further understand and remedy for the vulnerabilities, the detectability and identifiability for severe cyber-attacks are defined and characterized, which are followed by the discussions on the methodologies for cyber-attack detection and identification. Last but not least, based on the understanding of identifiability, a framework of resilient control design is proposed to mitigate the impact of cyber-attacks, which can be generalized in future to account for additional design criteria.</p>

A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems as well as novel AoI-aware scheduling policies accounting for the energy constraints at the transmitter nodes (for several settings of communication networks) in the process of decision-making using tools from optimization theory and reinforcement learning. The first part of this dissertation develops a stochastic hybrid system (SHS)-based general framework to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. First, we study a general setting of status updating systems, where a set of source nodes provide status updates about some physical process(es) to a set of monitors. For this setting, the continuous state of the system is formed by the AoI/age processes at different monitors, the discrete state of the system is modeled using a finite-state continuous-time Markov chain, and the coupled evolution of the continuous and discrete states of the system is described by a piecewise linear SHS with linear reset maps. Using the notion of tensors, we derive a system of linear equations for the characterization of the joint moment generating function (MGF) of an arbitrary set of age processes in the network. Afterwards, we study a general setting of gossip networks in which a source node forwards its measurements (in the form of status updates) about some observed physical process to a set of monitoring nodes according to independent Poisson processes. Furthermore, each monitoring node sends status updates about its information status (about the process observed by the source) to the other monitoring nodes according to independent Poisson processes. For this setup, we develop SHS-based methods that allow the characterization of higher-order marginal/joint moments of the age processes in the network. Finally, our SHS-based framework is applied to derive the stationary marginal and joint MGFs for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. The status updates of each source and harvested energy packets are assumed to arrive at the transmitter according to independent Poisson processes, and the service time of each status update is assumed to be exponentially distributed. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter, including non-preemptive and source-agnostic/source-aware preemptive in service strategies. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of an unmanned aerial vehicle (UAV) as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. In order to solve this non-convex problem, we propose an efficient iterative algorithm and establish its convergence analytically. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which radio frequency (RF)-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables. / Doctor of Philosophy / A typical model for real-time status update systems consists of a transmitter node that generates real-time status updates about some physical process(es) of interest and sends them through a communication network to a destination node. Such a model can be used to analyze the performance of a plethora of emerging Internet of Things (IoT)-enabled real-time applications including healthcare, factory automation, autonomous vehicles, and smart homes, to name a few. The performance of these applications highly depends upon the freshness of the information status at the destination node about its monitored physical process(es). Because of that, the main design objective of such real-time status update systems is to ensure timely delivery of status updates from the transmitter node to the destination node. To measure the freshness of information at the destination node, the Age of Information (AoI) has been introduced as a performance metric that accounts for the generation time of each status update (which was ignored by conventional performance metrics, specifically throughput and delay). Since then, there have been two main research directions in the AoI research area. The first direction aimed to analyze/characterize AoI in different queueing-theoretic models/disciplines, and the second direction was focused on the optimization of AoI in different communication systems that deal with time-sensitive information. However, the prior queueing-theoretic analyses of AoI have mostly been limited to the characterization of the average AoI and the prior studies developing AoI/age-aware scheduling/transmission policies have mostly ignored the energy constraints at the transmitter node(s). Motivated by these limitations, this dissertation first develops new queueing-theoretic methods that allow the characterization of the distribution of AoI in several classes of status updating systems. Afterwards, using tools from optimization theory and reinforcement learning, novel AoI-aware scheduling policies are developed while accounting for the energy constraints at the transmitter nodes for several settings of communication networks, including unmanned aerial vehicles (UAVs)-assisted and radio frequency (RF)-powered communication networks, in the process of decision-making. In the first part of this dissertation, a stochastic hybrid system (SHS)-based general framework is first developed to facilitate the analysis of characterizing the distribution of AoI in several classes of real-time status updating systems. Afterwards, this framework is applied to derive the stationary marginal and joint moment generating functions (MGFs) for several queueing disciplines and gossip network topologies, using which we derive closed-form expressions for marginal/joint high-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. In the second part of this dissertation, our analysis is focused on understanding the distributional properties of AoI in status updating systems powered by energy harvesting (EH). In particular, we consider a multi-source status updating system in which an EH-powered transmitter node has multiple sources generating status updates about several physical processes. The status updates are then sent to a destination node where the freshness of each status update is measured in terms of AoI. For this setup, we derive closed-form expressions of MGF of AoI under several queueing disciplines at the transmitter. The generality of our analysis is demonstrated by recovering several existing results as special cases. A key insight from our characterization of the distributional properties of AoI is that it is crucial to incorporate the higher moments of AoI in the implementation/optimization of status updating systems rather than just relying on its average (as has been mostly done in the existing literature on AoI). In the third and final part of this dissertation, we employ AoI as a performance metric for several settings of communication networks, and develop novel AoI-aware scheduling policies using tools from optimization theory and reinforcement learning. First, we investigate the role of a UAV as a mobile relay to minimize the average peak AoI for a source-destination pair. For this setup, we formulate an optimization problem to jointly optimize the UAV's flight trajectory as well as energy and service time allocations for packet transmissions. This optimization problem is subject to the UAV's mobility constraints and the total available energy constraints at the source node and UAV. A key insight obtained from our results is that the optimal design of the UAV's flight trajectory achieves significant performance gains especially when the available energy at the source node and UAV is limited and/or when the size of the update packet is large. Afterwards, we study a generic system setup for an IoT network in which RF-powered IoT devices are sensing different physical processes and need to transmit their sensed data to a destination node. For this generic system setup, we develop a novel reinforcement learning-based framework that characterizes the optimal sampling policy for IoT devices with the objective of minimizing the long-term weighted sum of average AoI values in the network. Our analytical results characterize the structural properties of the age-optimal policy, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. They further demonstrate that the structures of the age-optimal and throughput-optimal policies are different. Finally, we analytically characterize the structural properties of the AoI-optimal joint sampling and updating policy for wireless powered communication networks while accounting for the costs of generating status updates in the process of decision-making. Our results demonstrate that the AoI-optimal joint sampling and updating policy has a threshold-based structure with respect to different system state variables.

No description available.

We develop the robust control theories of stopping-time nonlinear systems and switching-control nonlinear systems. We formulate a robust optimal stopping-time control problem for a state-space nonlinear system and give the connection between various notions of lower value function for the associated game (and storage function for the associated dissipative system) with solutions of the appropriate variational inequality (VI). We show that the stopping-time rule can be obtained by solving the VI in the viscosity sense. It also happens that a positive definite supersolution of the VI can be used for stability analysis. We also show how to solve the VI for some prototype examples with one-dimensional state space. For the robust optimal switching-control problem, we establish the Dynamic Programming Principle (DPP) for the lower value function of the associated game and employ it to derive the appropriate system of quasivariational inequalities (SQVI) for the lower value vector function. Moreover we formulate the problem in the <I>L</I>₂-gain/dissipative system framework. We show that, under appropriate assumptions, continuous switching-storage (vector) functions are characterized as viscosity supersolutions of the SQVI, and that the minimal such storage function is equal to the lower value function for the game. We show that the control strategy achieving the dissipative inequality is obtained by solving the SQVI in the viscosity sense; in fact this solution is also used to address stability analysis of the switching system. In addition we prove the comparison principle between a viscosity subsolution and a viscosity supersolution of the SQVI satisfying a boundary condition and use it to give an alternative derivation of the characterization of the lower value function. Finally we solve the SQVI for a simple one-dimensional example by a direct geometric construction. / Ph. D.

Dans cette thèse nous développons des structures de supervision permettant de définir des consignes optimales pour des actionneurs thermiques, ainsi que des stratégies de commande appropriées pour le pilotage d’une pompe à chaleur (PAC). Pour répondre à ces objectifs, plusieurs étapes ont été réalisées :- Modélisation orientée commande d’une PAC réversible, des thermistances, et de l’environnement permettant de les lier à l’intérieur de l’habitacle. Des modèles physiques ont été définis et intégrés dans une plateforme du type Model-in-the-Loop pour permettre a posteriori la validation des stratégies de commande et d’optimisation. - Commande d’une PAC. La linéarisation du modèle de PAC autour de certains points de fonctionnement a permis le développement de la commande de l’actionneur principal. La structure de commande proposée permet de prendre en compte, en boucle fermée, des contraintes d’état et d’entrée du système. Les performances de cette structure ont été analysées en considérant successivement des régulateurs principaux de type PI et Hinf. Enfin, des algorithmes réalisant le pilotage d’un actionneur secondaire du système ont été également proposés. - Optimisation des actionneurs thermiques. L’utilisation combinée de thermistances et de la PAC présente des avantages en termes de réduction de la consommation énergétique et/ou du maintien de la puissance thermique demandée dans des conditions aux limites de fonctionnement. Le problème d’optimisation a été résolu en deux temps : des solutions hors-ligne ont été obtenues par résolution d’un problème mixte en nombre entier avec modèle prédictif, puis utilisées pour déduire des stratégies embarquables sur le véhicule. / Through this thesis we develop some strategies to define the optimal set points for thermal actuators, as well as an adequate control strategy for a heat pump. To this extent, different steps were carried out:- Control-oriented modeling of a reversible heat pump, thermistors, and the environment connecting these elements to the interior of the car’s cabin. Simplified but non-linear physical models were defined and used to build a Model-in-the-Loop platform, which would later be considered for the validation of the control and optimization strategies. - Control of a heat pump. The linearization of the heat pump model around some operating points was used to develop the control of the electric compressor, being the main actuator. The proposed control structure takes into account, in closed loop, input and state constraints. The performance of the structure was analyzed by using main controllers of PI and Hinf type. Lastly, some control algorithms were also proposed to control a second actuator of the system. - Thermal actuators optimization. Despite the high efficiency of the heat pump, the use of thermistors can be advantageous both for reducing the energetic consumption and/or to ensure the thermal power requests in extreme conditions. The optimization problem was carried out in two steps: offline solutions were firstly obtained solving a mixed-integer problem with predictive model, then used to derive some strategies that could be embedded in the vehicle.

Actuellement la nécessité des systèmes d’alimentation d’énergie, capables d’assurer un fonctionnement stable dans des domaines de fonctionnement assez larges avec des bonnes performances dynamiques (rapidité du système, variations limitées de la tension de sortie en réponse aux perturbations de charge ou de tension d’alimentation), devient de plus en plus importante. De ce fait, cette thèse est orientée sur la commande des convertisseurs de puissance DC-DC représentés par des modèles hybrides.En tenant compte de la structure variable de ces systèmes à commutation, un modèle hybride permet de décrire plus précisément le comportement dynamique d’un convertisseur dans son domaine de fonctionnement. Dans cette optique, l’approximation PWA est utilisée afin de modéliser les convertisseurs DC-DC. A partir des modèles hybrides développés, on s’est intéressé à la stabilisation des convertisseurs au moyen des correcteurs à gains commutés élaborés sur la base de fonctions de Lyapunov PWQ, et à l’implantation d’une commande prédictive explicite, en considérant des contraintes sur l’entrée de commande. La méthode de modélisation et les stratégies de commande proposées ont été appliquées sur deux topologies : un convertisseur buck, afin de mieux maîtriser le réglage des correcteurs et un convertisseur flyback avec filtre d’entrée. Cette dernière topologie nous a permis de répondre aux difficultés du point de vue de la commande (comportement à déphasage non-minimal) rencontrées dans la majorité des convertisseurs DC-DC. Les performances des commandes élaborées ont été validées en simulation sur les topologies considérées et expérimentalement sur une maquette du convertisseur buck. / Lately, power supply systems, guaranteeing the global stability for large enough operation ranges with good dynamic performances (small settling time, bounded overshoot of the output voltage in the presence of load or supply voltage variations), are strongly needed. Therefore, this thesis deals with control problems of DC-DC power converters represented by hybrid models.Considering the variable structure of these switched systems, a hybrid model describes more precisely the converter’s dynamics in its operating domain. From this perspective, a PWA (piecewise affine) approximation is used in order to model the DC-DC converters. Based on the developed hybrid models, first we have designed a stable piecewise linear state-feedback controller using piecewise quadratic (PWQ) Lyapunov functions, and secondly, we have implemented an explicit predictive control law taking into account constraints on the control input. The hybrid modeling technique and the proposed control strategies were applied on two different topologies of converters: a buck converter, in order to have a thorough knowledge of the controllers’ tuning, and a flyback converter with an input filter. This last topology, allowed us to manage different control problems (non-minimum phase behavior) encountered in the majority of topologies of DC-DC power converters. The controllers’ performances were validated in simulation on both considered topologies and also experimentally on buck converter.