<p>  </p> <p>The development of an increasingly interconnected infrastructure and its rapid evolution demands engineering testing solutions capable of investigating realistically and with high accuracy the interactions among the different components of the problem to study. The examination of any of these components without losing the interaction of the other surroundings components is not only realistic, but also desirable. The more interconnected the whole system is, the greater the dependencies. Real-time Hybrid Simulation (RTHS) is a disruptive technology that has the potential to address this type of complex interactions or internal couplings by partitioning the system into numerical (better understood) substructures and experimental (unknown) substructures, which are built physically in the laboratory. These two types of substructures are connected through a transfer system (e.g., hydraulic actuators) to enforce boundary conditions in their common interfaces creating a synchronized cyber-physical system. However, despite the RTHS community has been improving these hybrid techniques, there are still important barriers in their core methodologies. Current control approaches developed for RTHS were validated mainly for linear applications with limited capabilities to deal with high uncertainties, hard nonlinearities, or extensive damage of structural elements due to plasticity. Furthermore, capturing the realistic dynamics of a structural system requires the description of the motion using more than one degree of freedom, which increases the number of hydraulic actuators needed to enforce additional degrees of freedom at boundary condition interface. As these requirements escalate for larger or more complex problems, the computational cost can turn into a prohibitive constraint. </p> <p>In this dissertation, the main research goal is to develop and validate a nonlinear controller with capabilities to control highly uncertain nonlinear physical substructures with complex boundary conditions and its parallel computational implementation for accurate and realistic RTHS. The validation of the proposed control system is achieved through a set of real-time tracking control and RTHS experiments that explore robustness, accuracy performance, and their trade-off </p>

La résolution d'un problème d'estimation de l'état d'un système nécessite de disposer d'un modèle régissant l'évolution des variables d'état et de mesurer de manière directe ou indirecte l'ensemble ou une partie de ces variables d'état. Les travaux exposés dans ce mémoire de thèse portent sur la problématique d'estimation en présence de méconnaissances de modèle et de pertes de capteurs. La première partie de ce travail constitue la synthèse d'un dispositif d'estimation d'état pour systèmes non linéaires. Cela consiste à sélectionner un estimateur d'état et convenablement le régler, puis à concevoir algorithmiquement, à partir d'un critère introduit pour la circonstance, une redondance matérielle visant à compenser la perte de certains capteurs. La seconde partie de ce travail porte sur la conception, à l'aide de la variance d'Allan, d'un sous-modèle permettant de compenser les incertitudes d'un modèle d'état, ce sous-modèle étant utilisable par un filtre de Kalman. Ce travail a été exploité pour tenir compte de dérives gyroscopiques dans le cadre d'une navigation inertielle hybridée avec des mesures GPS par un filtre de Kalman contraint. Les résultats obtenus, issus d'expériences sur deux trajectoires d'avion, ont montré un comportement sain et robuste de l'approche proposée / To solve the problem of estimating the state of a system, it is necessary to have at one's disposal a model governing the dynamic of the state variables and to measure directly or indirectly all or a part of these variables. The work presented in this thesis deals with the estimation issue in the presence of model uncertainties and sensor losses. The first part of this work represents the synthesis of a state estimation device for nonlinear systems. It consists in selecting a state estimator and properly tuning it. Then, thanks to a criterion introduced for the occasion, it consists in algorithmically designing a hardware redundancy aiming at compensating for some sensor losses. The second part of this work deals with the conception of a sub-model compensating for some model uncertainties. This sub-model, designed by using the Allan variance, is usable by a Kalman filter. This work has been used to take into account some gyroscopical drifts in a GPS-INS integrated navigation based on a constrained Kalman filter. The results obtained, coming from experiments on two plane trajectories, showed a safe and robust behaviour of the proposed method