Dans ce mémoire, nous étudions un système prédateur-proie de Gause généralisé avec une récolte de proie contrôlée et une fonction de réponse de Holling de type III généralisée. Nous introduisons une fonction de récolte contrôlée sur les proies tenant compte du nombre de proies et dépendant d'un seuil de récolte. Ceci permet de rendre le système réaliste, d'optimiser la récolte, et de prévenir la possibilité d'extinction des espèces que le système avec récolte constante pouvait avoir pour toutes valeurs de paramètres. Ce type de fonction de récolte implique a priori la manipulation d'un système discontinu: nous étudions donc des techniques de lissage de ces discontinuités par régularisation. Nous faisons d'abord un retour sur les systèmes sans et avec récolte de proie constante en traçant les diagrammes de bifurcations exacts et les portraits de phase de ces systèmes. Ensuite, nous étudions le système discontinu et les méthodes de régularisation afin de choisir la plus optimale. Finalement, nous assemblons le tout avec l'étude du système avec récolte de proie régularisé, en passant par l'étude complète du système avec approvisionnement de proie, et donnons les différents effets sur les portraits de phase selon les conditions initiales. / In this master thesis, we study a generalized Gause predator-prey system with controlled prey harvest and a generalized Holling response function of type III. We introduce a controlled prey harvesting function taking into account the number of preys with a harvesting threshold. This makes the system realistic, it optimizes the harvesting, and it prevents the possibility of species' extinction which exists in the system with constant harvest for all parameters. This type of harvesting function a priori implies handling a discontinuous system : therefore we study smoothing techniques of such discontinuities by regularization. We first return on systems without and with constant harvest by drawing the exact bifurcation diagrams and phase portraits of those systems. Then, we study the discontinuous system and the regularization methods in order to choose the optimal one. Finally, we put together everything by studying the regularized prey harvesting system through a complete study of the prey stocking system, and we highlight the different effects on the phase portraits under the initial conditions.

In biochemical networks transient dynamics plays a fundamental role, since the activation of signalling pathways is determined by thresholds encountered during the transition from an initial state (e.g. an initial concentration of a certain protein) to a steady-state. These thresholds can be defined in terms of the inflection points of the stimulus-response curves associated to the activation processes in the biochemical network. In the present work, we present a rigorous discussion as to the suitability of finite-time Lyapunov exponents and metabolic control coefficients for the detection of inflection points of stimulus-response curves with sigmoidal shape.

In this thesis, we study the existence of pathwise random periodic solutions to both the semilinear stochastic differential equations with linear multiplicative noise and the semilinear stochastic partial differential equations with linear multiplicative noise in a Hilbert space. We identify them as the solutions of coupled forward-backward infinite horizon stochastic integral equations in general cases, and then perform the argument of the relative compactness of Wiener-Sobolev spaces in C([0, T],L2Ω,Rd)) or C([0, T],L2(Ω x O)) and Schauder's fixed point theorem to show the existence of a solution of the coupled stochastic forward-backward infinite horizon integral equations.

La maladie de Newcastle (MN) grève lourdement les productions aviaires malgaches, essentielles à l'alimentation et à l'économie familiales. La MN est une dominante pathologique en l'absence de vaccination généralisée. L'objectif de cette thèse est la modélisation, la validation et l'analyse mathématique de modèles de transmission du virus de la MN (VMN) dans les systèmes avicoles villageois en général et à Madagascar en particulier. Nous proposons de nouveaux modèles basés sur les connaissances actuelles de l'histoire naturelle de la transmission du VMN. Ainsi, nous présentons deux modèles mathématiques à compartiments de la transmission du VMN dans une population de poules : un premier modèle avec transmission environnementale et un deuxième modèle où la vaccination contre la maladie est prise en compte. Nous présentons une analyse complète de la stabilité de ces modèles à l'aide des techniques de Lyapunov suivant la valeur du taux de reproduction de base R0. Le travail s'est appuyé sur des enquêtes de terrain pour comprendre les pratiques de vaccination actuelles à Madagascar. / Newcastle disease (ND) severely harms Malagasy bird productions, mainly uses to food and family economy. ND is a pathological dominant without general vaccination. The objective of this thesis is modelling the transmission of ND virus (NDV) in smallholder chicken farms in general and, Madagascar in particular. We propose new models based on the state of art and the epidemiology currently known from the transmission of the NDV. Thus, we present two models of the transmission of NDV: a first model with environmental transmission and a second model in which imperfect vaccination of chickens is considered. We present a thorough analysis of the stability of the models using the Lyapunov techniques and obtain the basic reproduction ratio R0. This work is based on field surveys to understand the current vaccination practices in Madagascar.

Im Beitrag wird zunächst die konventionelle auf Signalflüssen basierte Modellbildung mit modernen leistungsflussbasierten Methoden, die auf dem Prinzip von Aktion und Reaktion aufbauen, verglichen. BG (Bond Graph), POG (Power Oriented Graph) und EMR (Energetic Macroscopic representation) sind solche modernen Methoden die den Leistungsaustausch zwischen Teilsystemen als Grundlage für den Modellbildungsansatz nutzen. Diese Werkzeuge erhalten die physikalische Struktur, erlauben es in das dynamische System hineinzuschauen und unterstützen das Verständnis des Leistungsflusses. Unterschiede werden anhand verschiedener Eigenschaften in einer Tabelle angegeben. Nach Erläuterung der Grundlagen zu POG und BG erfolgt die Vorstellung einer Freeware Zusatzbibliothek zur Simulation von Bondgraphen. Spezielle Eigenschaften werden kurz umrissen. Diese Blockbibliothek läuft unter Simulink, besteht aus nur 9 mittels Menü konfigurierbaren Blöcken und realisiert bidirektionale Verbindungen. Die Beispiele Gleichstrommotor, Pulssteller und elastische Welle demonstrieren die Vorteile der leistungsflussorientierten Modellbildung. Zustandsregelung, Energieeffizienz und Simulink LTI Analysewerkzeuge führen in die Anwendung der vorgestellten Simulink Zusatzbibliothek für Bondgraphen ein. / The paper starts with a comparison of the conventional modelling method based on signal flow and modern power flow oriented modelling methods based on the principle of action and reaction. BG (Bond Graph), POG (Power Oriented Graph) and EMR (Energetic Macroscopic representation) are such modern methods based on the power exchange between partial systems as a key element for the basic modelling approach. These tools preserve the physical structure, enable a view inside dynamical systems and support understanding the power flow. Relationships between these graphical representations will be given. After the explanation of basics for POG and BG an overview and special features of a freeware add-on library for simulation of BGs will be outlined. The block library runs under Simulink, consists of nine menu-driven customised blocks only and realises bidirectional connections. Examples DC motor, chopper and elastic shaft demonstrate the advantages of power flow oriented modelling. State space control, energy efficiency and Simulink LTI analysis tools exemplify the application of the presented Simulink add-on BG library.

A hybrid system is a dynamical system that exhibits both continuous and discrete dynamic behavior. Hybrid systems arise in a wide variety of important applications in diverse areas, ranging from biology to computer science to air traffic dynamics. The interaction of continuous- and discrete-time dynamics in a hybrid system often leads to very rich dynamical behavior and phenomena that are not encountered in purely continuous- or discrete-time systems. Investigating the dynamical behavior of hybrid systems is of great theoretical and practical importance. The objectives of this thesis are to develop the qualitative theory of nonlinear hybrid systems with impulses, time-delay, switching modes, and stochastic disturbances, to develop algorithms and perform analysis for hybrid systems with an emphasis on stability and control, and to apply the theory and methods to real-world application problems. Switched nonlinear systems are formulated as a family of nonlinear differential equations, called subsystems, together with a switching signal that selects the continuous dynamics among the subsystems. Uniform stability is studied emphasizing the situation where both stable and unstable subsystems are present. Uniformity of stability refers to both the initial time and a family of switching signals. Stabilization of nonlinear systems via state-dependent switching signal is investigated. Based on assumptions on a convex linear combination of the nonlinear vector fields, a generalized minimal rule is proposed to generate stabilizing switching signals that are well-defined and do not exhibit chattering or Zeno behavior. Impulsive switched systems are hybrid systems exhibiting both impulse and switching effects, and are mathematically formulated as a switched nonlinear system coupled with a sequence of nonlinear difference equations that act on the switched system at discrete times. Impulsive switching signals integrate both impulsive and switching laws that specify when and how impulses and switching occur. Invariance principles can be used to investigate asymptotic stability in the absence of a strict Lyapunov function. An invariance principle is established for impulsive switched systems under weak dwell-time signals. Applications of this invariance principle provide several asymptotic stability criteria. Input-to-state stability notions are formulated in terms of two different measures, which not only unify various stability notions under the stability theory in two measures, but also bridge this theory with the existent input/output theories for nonlinear systems. Input-to-state stability results are obtained for impulsive switched systems under generalized dwell-time signals. Hybrid time-delay systems are hybrid systems with dependence on the past states of the systems. Switched delay systems and impulsive switched systems are special classes of hybrid time-delay systems. Both invariance property and input-to-state stability are extended to cover hybrid time-delay systems. Stochastic hybrid systems are hybrid systems subject to random disturbances, and are formulated using stochastic differential equations. Focused on stochastic hybrid systems with time-delay, a fundamental theory regarding existence and uniqueness of solutions is established. Stabilization schemes for stochastic delay systems using state-dependent switching and stabilizing impulses are proposed, both emphasizing the situation where all the subsystems are unstable. Concerning general stochastic hybrid systems with time-delay, the Razumikhin technique and multiple Lyapunov functions are combined to obtain several Razumikhin-type theorems on both moment and almost sure stability of stochastic hybrid systems with time-delay. Consensus problems in networked multi-agent systems and global convergence of artificial neural networks are related to qualitative studies of hybrid systems in the sense that dynamic switching, impulsive effects, communication time-delays, and random disturbances are ubiquitous in networked systems. Consensus protocols are proposed for reaching consensus among networked agents despite switching network topologies, communication time-delays, and measurement noises. Focused on neural networks with discontinuous neuron activation functions and mixed time-delays, sufficient conditions for existence and uniqueness of equilibrium and global convergence and stability are derived using both linear matrix inequalities and M-matrix type conditions. Numerical examples and simulations are presented throughout this thesis to illustrate the theoretical results.

We consider a simple one-dimensional random dynamical system with two driving vector fields and random switchings between them. We show that this system satisfies a one force - one solution principle and compute its unique invariant density explicitly. We study the limiting behavior of the invariant density as the switching rate approaches zero and infinity and derive analogues of classical probabilistic results such as the central limit theorem and large deviations principle.

A hybrid system is a dynamical system that exhibits both continuous and discrete dynamic behavior. Hybrid systems arise in a wide variety of important applications in diverse areas, ranging from biology to computer science to air traffic dynamics. The interaction of continuous- and discrete-time dynamics in a hybrid system often leads to very rich dynamical behavior and phenomena that are not encountered in purely continuous- or discrete-time systems. Investigating the dynamical behavior of hybrid systems is of great theoretical and practical importance. The objectives of this thesis are to develop the qualitative theory of nonlinear hybrid systems with impulses, time-delay, switching modes, and stochastic disturbances, to develop algorithms and perform analysis for hybrid systems with an emphasis on stability and control, and to apply the theory and methods to real-world application problems. Switched nonlinear systems are formulated as a family of nonlinear differential equations, called subsystems, together with a switching signal that selects the continuous dynamics among the subsystems. Uniform stability is studied emphasizing the situation where both stable and unstable subsystems are present. Uniformity of stability refers to both the initial time and a family of switching signals. Stabilization of nonlinear systems via state-dependent switching signal is investigated. Based on assumptions on a convex linear combination of the nonlinear vector fields, a generalized minimal rule is proposed to generate stabilizing switching signals that are well-defined and do not exhibit chattering or Zeno behavior. Impulsive switched systems are hybrid systems exhibiting both impulse and switching effects, and are mathematically formulated as a switched nonlinear system coupled with a sequence of nonlinear difference equations that act on the switched system at discrete times. Impulsive switching signals integrate both impulsive and switching laws that specify when and how impulses and switching occur. Invariance principles can be used to investigate asymptotic stability in the absence of a strict Lyapunov function. An invariance principle is established for impulsive switched systems under weak dwell-time signals. Applications of this invariance principle provide several asymptotic stability criteria. Input-to-state stability notions are formulated in terms of two different measures, which not only unify various stability notions under the stability theory in two measures, but also bridge this theory with the existent input/output theories for nonlinear systems. Input-to-state stability results are obtained for impulsive switched systems under generalized dwell-time signals. Hybrid time-delay systems are hybrid systems with dependence on the past states of the systems. Switched delay systems and impulsive switched systems are special classes of hybrid time-delay systems. Both invariance property and input-to-state stability are extended to cover hybrid time-delay systems. Stochastic hybrid systems are hybrid systems subject to random disturbances, and are formulated using stochastic differential equations. Focused on stochastic hybrid systems with time-delay, a fundamental theory regarding existence and uniqueness of solutions is established. Stabilization schemes for stochastic delay systems using state-dependent switching and stabilizing impulses are proposed, both emphasizing the situation where all the subsystems are unstable. Concerning general stochastic hybrid systems with time-delay, the Razumikhin technique and multiple Lyapunov functions are combined to obtain several Razumikhin-type theorems on both moment and almost sure stability of stochastic hybrid systems with time-delay. Consensus problems in networked multi-agent systems and global convergence of artificial neural networks are related to qualitative studies of hybrid systems in the sense that dynamic switching, impulsive effects, communication time-delays, and random disturbances are ubiquitous in networked systems. Consensus protocols are proposed for reaching consensus among networked agents despite switching network topologies, communication time-delays, and measurement noises. Focused on neural networks with discontinuous neuron activation functions and mixed time-delays, sufficient conditions for existence and uniqueness of equilibrium and global convergence and stability are derived using both linear matrix inequalities and M-matrix type conditions. Numerical examples and simulations are presented throughout this thesis to illustrate the theoretical results.

The topology of interactions in network dynamical systems fundamentally underlies their function. Accelerating technological progress creates massively available data about collective nonlinear dynamics in physical, biological, and technological systems. Detecting direct interaction patterns from those dynamics still constitutes a major open problem. In particular, current nonlinear dynamics approaches mostly require to know a priori a model of the (often high dimensional) system dynamics. Here we develop a model-independent framework for inferring direct interactions solely from recording the nonlinear collective dynamics generated. Introducing an explicit dependency matrix in combination with a block-orthogonal regression algorithm, the approach works reliably across many dynamical regimes, including transient dynamics toward steady states, periodic and non-periodic dynamics, and chaos. Together with its capabilities to reveal network (two point) as well as hypernetwork (e.g., three point) interactions, this framework may thus open up nonlinear dynamics options of inferring direct interaction patterns across systems where no model is known.

Cette thèse est consacrée à l'étude de la dynamique des systèmes complexes. Nous construisons des réseaux couplés à partir de multiples instances de systèmes dynamiques déterministes, donnés par des équations différentielles ordinaires ou des équations aux dérivées partielles de type parabolique, qui décrivent un problème d'évolution. Nous étudions le lien entre la dynamique interne à chaque nœud du réseau, les éléments de la topologie du graphe portant ce réseau, et sa dynamique globale. Nous recherchons les conditions de couplage qui favorisent une dynamique globale particulière à l'échelle du réseau, et étudions l'impact des interactions sur les bifurcations identifiées sur chaque nœud. Nous considérons en particulier des réseaux couplés de systèmes de réaction-diffusion, dont nous étudions le comportement asymptotique, en recherchant des régions positivement invariantes, et en démontrant l'existence d'attracteurs exponentiels de dimension fractale finie, à partir d'estimations d'énergie qui révèlent la nature dissipative de ces réseaux de systèmes de réaction-diffusion. Ces questions sont étudiées dans le cadre de quelques applications. En particulier, nous considérons un modèle mathématique pour l'étude géographique des réactions comportementales d'individus, au sein d'une population en situation de catastrophe. Nous présentons les éléments de modélisation associés, ainsi que son étude mathématique, avec une analyse de la stabilité des équilibres et de leurs bifurcations. Nous établissons l'importance capitale des chemins d'évacuation dans les réseaux complexes construits à partir de ce modèle, pour atteindre l'équilibre attendu de retour au comportement du quotidien pour l'ensemble de la population considérée, tout en évitant une propagation du comportement de panique. D'autre part, la recherche de solutions périodiques émergentes dans les réseaux d'oscillateurs nous amène à considérer des réseaux complexes de systèmes hamiltoniens pour lesquels nous construisons des perturbations polynomiales qui provoquent l'apparition de cycles limites, problématique liée au XVIème problème de Hilbert. / This thesis is devoted to the study of the dynamics of complex systems. We consider coupled networks built with multiple instances of deterministicdynamical systems, defined by ordinary differential equations or partial differential equations of parabolic type, which describe an evolution problem.We study the link between the internal dynamics of each node in the network, its topology, and its global dynamics. We analyze the coupling conditions which favor a particular dynamics at the network's scale, and study the impact of the interactions on the bifurcations identified on each node. In particular, we consider coupled networks of reaction-diffusion systems; we analyze their asymptotic behavior by searching positively invariant regions, and proving the existence of exponential attractors of finite fractal dimension, derived from energy estimates which suggest the dissipative nature of those networks of reaction-diffusion systems.Our framework includes the study of multiple applications. Among them, we consider a mathematical model for the geographical analysis of behavioral reactions of individuals facing a catastrophic event. We present the modeling choices that led to the study of this evolution problem, and its mathematical study, with a stability and bifurcation analysis of the equilibria. We highlight the decisive role of evacuation paths in coupled networks built from this model, in order to reach the expected equilibrium corresponding to a global return of all individuals to the daily behavior, avoiding a propagation of panic. Furthermore, the research of emergent periodic solutions in complex networks of oscillators brings us to consider coupled networks of hamiltonian systems, for which we construct polynomial perturbationswhich provoke the emergence of limit cycles, question which is related to the sixteenth Hilbert's problem.