A new suboptimal guidance law design approach for aerospace vehicles is proposed in this thesis, followed by an advanced control design for supersonic air-breathing engines. The guidance law is designed using the newly developed Generalized Model Predictive Static Programming (G-MPSP), which is based on the continuous time nonlinear optimal control framework. The key feature of this technique is one-time backward propagation of a small-dimensional weighting matrix dynamics, which is used to update the entire control history. This key feature, as well as the fact that it leads to a static optimization problem, lead to its computational efficiency. It has also been shown that the existing model predictive static programming (MPSP), which is based on the discrete time framework, is a special case of G-MPSP. The G-MPSP technique is further extended to incorporate ‘input inequality constraints’ in a limited sense using the penalty function philosophy. Next, this technique has been developed also further in a ‘flexible final time’ framework to converge rapidly to meet very stringent final conditions with limited number of iterations. Using the G-MPSP technique in a flexible final time and input inequality constrained formulation, a suboptimal guidance law for a solid motor propelled carrier launch vehicle is successfully designed for a hypersonic mission. This guidance law assures very stringent final conditions at the injection point at the end of the guidance phase for successful beginning of the hypersonic vehicle operation. It also ensures that the angle of attack and structural load bounds are not violated throughout the trajectory. A second-order autopilot has been incorporated in the simulation studies to mimic the effect of the inner-loops on the guidance performance. Simulation studies with perturbations in the thrust-time behaviour, drag coefficient and mass demonstrate that the proposed guidance can meet the stringent requirements of the hypersonic mission. The G-MPSP technique in a fixed final time and input inequality constrained formulation has also been used for optimal guidance of an aerospace vehicle propelled by supersonic air-breathing engine, where the resulting thrust can be manipulated by managing the fuel flow and nozzle area (which is not possible in solid motors). However, operation of supersonic air-breathing engines is quite complex as the thrust produced by the engine is a result of very complex nonlinear combustion dynamics inside the engine. Hence, to generate the desired thrust, accounting for a fairly detailed engine model, a dynamic inversion based nonlinear state feedback control design has been carried out. The objective of this controller is to ensure that the engine dynamically produces the thrust that tracks the commanded value of thrust generated from the guidance loop as closely as possible by regulating the fuel flow rate. Simultaneously, by manipulating throat area of the nozzle, it also manages the shock wave location in the intake for maximum pressure recovery with sufficient margin for robustness. To filter out the sensor and process noises and to estimate the states for making the control design operate based on output feedback, an extended Kalman filter (EKF) based state estimation design has also been carried out and the controller has been made to operate based on estimated states. Moreover, independent control designs have also been carried out for the actuators so that their response can be faster. In addition, this control design becomes more challenging to satisfy the imposed practical constraints like fuel-air ratio and peak combustion temperature limits. Simulation results clearly indicate that the proposed design is quite successful in assuring the desired performance of the air-breathing engine throughout the flight trajectory, i.e., both during the climb and cruise phases, while assuring adequate pressure margin for shock wave management.

L'objectif de cette thèse est de proposer l'approche la plus appropriée afin de minimiser le coût d'intégration de petite éolienne dans un micro-réseau DC urbain. Une petit éolienne basé sur un machine synchrone à aimant permanent (MSAP) est considéré à étudier. Un état de l'art concernant les énergies renouvelables, micro-réseau DC, et la production d'énergie éolienne, est fait. Comme le capteur mécanique de cette structure est relativement d'un coût élevé, les différents types de contrôle pour un système de conversion éolienne sont présentés afin de choisir une structure active de conversion d'énergie et un MSAP sans capteur. Par conséquent, un estimateur de vitesse/position est nécessaire pour contrôler le système. Ainsi, les méthodes différentes proposées dans la littérature sont considérées et classifiées à étudier dans les détails, puis les plus efficaces et largement utilisés sont à vérifier dans la simulation et expérimentalement pour le système étudié. Les méthodes choisies sont: estimation de la flux de rotor avec boucle à verrouillage de phase (PLL), observateur à mode glissement (SMO), observateur de Luenberger d'ordre réduit, et filtre de Kalman étendu (EKF). Face à d'autres méthodes, l'estimateur basé sur un modèle EKF permet une commande sans capteur dans une large plage de vitesse et estime la vitesse de rotation avec une réponse rapide. Le réglage des paramètres EKF est le problème principal à sa mise en œuvre. Par conséquent, pour résoudre ce problème, la thèse présente une méthode adaptative, à savoir réglage-adaptatif d’EKF. En conséquence, et grâce à cette approche, le coût total du système de conversion est réduite et la performance est garantie et optimisée. / The aim of this thesis is to propose the most appropriate approach in order to minimize the cost of integration of a wind generator into a DC urban microgrid. A small-scale wind generator based on a permanent magnet synchronous machine (PMSM) is considered to be studied. A state of the art concerning the renewable energies, DC microgrid, and wind power generation is done. As the mechanical sensor for this structure is relatively of high cost, various types of wind conversion system control are presented in order to choose an energy conversion active structure and a sensorless PMSM. Therefore, a speed/position estimator is required to control the system. Thus, different methods proposed in literatures are considered and classified to be studied in details, and then the most effective and widely used ones are to be verified in simulation and experimentally for the studied system. The methods which are chosen are: rotor flux estimation with phase locked loop (PLL), sliding mode observer (SMO), Luenberger observer of reduced order, and extended Kalman filter (EKF). Facing to other methods, the EKF model-based estimator allows sensorless drive control in a wide speed range and estimates the rotation speed with a rapid response. The EKF parameters tuning is the main problem to its implementation. Hence, to solve this problem, the thesis introduces an adaptive method, i.e. adaptive-tuning EKF. As a result and grace to this approach, the total cost of conversion system is reduced and the performance is guaranteed and optimized.

Actuellement, les machines Asynchrones à Double Alimentation (MADA) sont omniprésentes dans le secteur éolien, grâce à leur simplicité de construction, leur faible coût d’achat et leur robustesse mécanique ainsi que le nombre faible d’interventions pour la maintenance. Cependant, comme toute autre machine électrique, ces génératrices sont sujettes aux défauts de différent ordre (électrique, mécanique, électromagnétique…) ou de différents types (capteur, actionneur ou composants du système). C’est pourquoi, il est primordial de concevoir une approche de diagnostic permettant de manière anticipée, de détecter, localiser et identifier tout défaut ou anomalie pouvant altérer le fonctionnement sain de ce type de machine. Motivés par les points forts des méthodes de diagnostic de défauts à base d’observateurs, nous proposons d’une part, dans cette thèse, une approche de détection, localisation et identification des défauts de la MADA d’une éolienne à vitesse variable, à base des observateurs de Kalman, performants et largement utilisés. Les erreurs d’estimation d’état du filtre de Kalman linéaire et de ses variantes non-linéaires, à noter : le Filtre de Kalman Etendu (EKF) et le Filtre de Kalman sans-Parfum (UKF), sont utilisés comme résidus sensibles aux défauts. En vue d’éviter les fausses alarmes et de découpler les défauts des perturbations et des bruits, l’analyse des résidus générés est réalisée par des tests statistiques tels que : Test de Page Hinkley (PH) et Test DCS (Dynamic Cumulative Sum). Pour la localisation des défauts multiples et simultanés, la Structure d’Observateurs Dédiés (DOS) et la Structure d’Observateurs Généralisés (GOS) sont appliquées. De plus, l’amplitude du défaut est déterminée dans l’étape d’identification de défaut. Les défauts capteurs, actionneurs et composants de la MADA, sont traités dans ce travail de recherche. D’autre part, une étude comparative entre les différents observateurs de Kalman, est élaborée. La comparaison porte sur les critères suivants : le temps de calcul, la précision et la vitesse de convergence des estimations. / Actually, the Doubly Fed Induction Generators (DFIG) are omnipresent in the wind power market, owing to their construction simplicity, their low purchase cost and their mechanical robustness. However, as any other electrical machine, these generators are subject to defects of different order (electrical, mechanical, electromagnetic ...) or of different type (sensor, actuator or system). That’s why, it is important to design an effective diagnostic approach, able to early detect, locate and identify any defect or abnormal behavior, which could undermine the healthy operation of this machine On the one hand, motivated by the observer-based fault diagnosis methods strengths, we proposed, in this thesis, a diagnostic approach for the faults detection, localization and identification of the DFIG used in variable speed wind turbine. This approach is based on the use of the efficient and widely used Kalman observers. The state estimation errors of the linear Kalman filter and the non-linear Kalman filters, named: The Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF) are used as faults sensitive residuals. In order to avoid false alarms and to decouple faults from disturbances and noises, the faults detection is carried out by the analysis of the residuals generated, by the mean of statistical tests such as: Hinkley Page Test (PH) and DCS Test (Dynamic) Cumulative Sum). For the localization step in case of multiple and simultaneous faults, the Dedicated Observer scheme (DOS) and the Generalized Observer scheme (GOS) are applied. In addition, the fault level is determined in the fault identification step. Sensor faults, actuator and system faults of DFIG, are treated in this research work. On the other hand, a comparative study between the three Kalman observers proposed is performed. The comparison was done in terms of (1) the computation time, (2) the estimation accuracy, and (3) the convergence speed.