Cette thèse développe une nouvelle approche de contrôle pour les avions à échelle réduite. Les lois de commande proposées exploitent un modèle non linéaire simple mais pertinent des forces aérodynamiques appliquées à l’aéronef. Ils reposent sur une structure hiérarchique de contrôle non linéaires, et sont synthétisées sur la base d’analyse de stabilité et de convergence théoriques. Ils sont conçus pour fonctionner sur un large domaine de vol. En particulier, ils évitent les singularités associées à la paramétrisation de l'attitude et la direction de la vitesse. Dans un premier temps, le problème de stabilisation de trajectoires de référence est résolu en étendant la méthode du "thrust vectoring", utilisée pour les véhicules à voilure tournante, au cas des aéronefs à voilure fixe. Dans le cas des avions, le principal défi est de prendre en compte les forces aérodynamiques dans la conception des systèmes de commande. Afin de résoudre ce problème, le contrôle proposé est conçu et analysé sur la base du modèle de forces aérodynamique proposé. Le domaine d'utilisation de cette loi de commande est élargi et englobe les trajectoires d'équilibre (trim trajectories) qui sont classiquement utilisées dans la littérature. Cette solution est ensuite adaptée au problème de suivi de chemin, afin de concevoir des lois de guidage cinématique et de contrôle dynamique applicables à presque tout chemin 3D régulier. Les lois de contrôle proposées contiennent des termes intégraux qui robustifient le contrôle vis-à-vis de dynamiques non modélisées. Plusieurs problèmes pratiques sont adressés et les lois de commande proposées sont validées par des simulations du type "hardware-in-the-loop". Enfin, des résultats d'essais en vol illustrent la performance des lois de contrôle proposées. / The present thesis develops a new control approach for scale-model airplanes. The proposed control solutions exploit a simple but pertinent nonlinear model of aerodynamic forces acting on the aircraft. Nonlinear controllers are based on a hierarchical structure, and are derived on the basis of theoretical stability and convergence analyses. They are designed to operate on a large spectrum of operating conditions. In particular, they avoid the singularities associated with the parameterization of the attitude and the heading of the vehicle, and do not rely on a decoupling between longitudinal and lateral dynamics. First, the trajectory tracking problem is addressed by extending the thrust vectoring method used for small rotor vehicles to the case of fixed wing vehicles. In the case of airplanes, the main challenge is to take into account the aerodynamic forces in the design of control systems. In order to solve this problem, the proposed control is designed and analyzed on the basis of the proposed aerodynamic forces model. The flight envelope is thus broadened beyond trim trajectories which are classically used in the literature. This solution is then adapted to the path following problem, and kinematic guidance and dynamic control laws are developed within a single coherent framework that applies to almost any regular 3D path. The proposed control laws incorporate integral terms that robustify the control with respect to unmodelled dynamics. Several practical issues are addressed and the proposed control laws are validated via hardware-in-the-loop simulations. Finally, successful flight test results illustrate the soundness and performance of the proposed control laws.

Cooperative control problems for multiple vehicle systems can be categorized as either formation control problems with applications to mobile robots, unmanned air vehicles, autonomous underwater vehicles, satellites, aircraft, spacecraft, and automated highway systems, or non-formation control problems such as task assignment, cooperative transport, cooperative role assignment, air traffic control, cooperative timing, and cooperative search. The cooperative control of multiple vehicle systems poses significant theoretical and practical challenges. For cooperative control strategies to be successful, numerous issues must be addressed. We consider three important and correlated issues: consensus seeking, formation keeping, and trajectory tracking. For consensus seeking, we investigate algorithms and protocols so that a team of vehicles can reach consensus on the values of the coordination data in the presence of imperfect sensors, communication dropout, sparse communication topologies, and noisy and unreliable communication links. The main contribution of this dissertation in this area is that we show necessary and/or sufficient conditions for consensus seeking with limited, unidirectional, and unreliable information exchange under fixed and switching interaction topologies (through either communication or sensing). For formation keeping, we apply a so-called "virtual structure" approach to spacecraft formation flying and multi-vehicle formation maneuvers. As a result, single vehicle path planning and trajectory generation techniques can be employed for the virtual structure while trajectory tracking strategies can be employed for each vehicle. The main contribution of this dissertation in this area is that we propose a decentralized architecture for multiple spacecraft formation flying in deep space with formation feedback introduced. This architecture ensures the necessary precision in the presence of actuator saturation, internal and external disturbances, and stringent inter-vehicle communication limitations. A constructive approach based on the satisficing control paradigm is also applied to multi-robot coordination in hardware. For trajectory tracking, we investigate nonlinear tracking controllers for fixed wing unmanned air vehicles and nonholonomic mobile robots with velocity and heading rate constraints. The main contribution of this dissertation in this area is that our proposed tracking controllers are shown to be robust to input uncertainties and measurement noise, and are computationally simple and can be implemented with low-cost, low-power microcontrollers. In addition, our approach allows piecewise continuous reference velocity and heading rate and can be extended to derive a variety of other trajectory tracking strategies.

Unmanned vehicle systems, specifically unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs), have become a popular research topic. This thesis discusses the potential of a UAV-UGV system used to track a human moving through complex urban terrain. This research focuses on path planning problems for both a UAV and a UGV, and presents effective solutions for both problems. In the UAV path planning problem, we desire to plan a path for a miniature fixed-wing UAV to fly through known urban terrain without colliding with any buildings. We present the Waypoint RRT (WRRT) algorithm, which accounts for UAV dynamics while planning a flyable, collision-free waypoint path for a UAV in urban terrain. Results show that this method is fast and robust, and is able to plan paths in difficult urban environments and other terrain maps as well. Simulation and hardware tests demonstrate that these paths are indeed flyable by a UAV. The UGV path planning problem focuses on planning a path to capture a moving target in an urban grid. We discuss using a target motion model based on Markov chains to predict future target locations. We then introduce the Capture and Propagate algorithm, which uses this target motion model to determine the probabilities of capturing the target in various numbers of steps and with various initial UGV moves. By applying some different cost functions, the result of this algorithm is used to choose an optimal first step for the UGV. Results demonstrate that this algorithm is at least as effective as planning a path directly to the current location of the target, and that in many cases, this algorithm performs better. We discuss these cases and verify them with simulation results.

No description available.

Les systèmes non linéaires sont si diverses que des outils communs de contrôle sont difficiles à développer. La théorie du contrôle non linéaire nécessite une analyse mathématique rigoureuse pour motiver ses conclusions. Cette thèse aborde deux branches distinctes et bien importantes de la théorie du contrôle non linéaire: le contrôle des systèmes non-linéaires incertains et le contrôle des systèmes sous-actionnés.Dans la première partie, une classe de contrôleurs par mode glissant d’ordre supérieur (MGOS) robuste, basée sur la synthèse de Lyapunov, est développée pour le contrôle des systèmes non-linéaires incertains. Cette classe de contrôleurs est basée sur une classe de régulateurs qui stabilisent une pure chaîne d’intégrateurs en temps fini, et nécessite la connaissance a priori des bornes sur les incertitudes du système. Puis, afin d’éliminer la dépendance liée à la connaissance de ces bornes, un contrôleur par MGOS adaptatif est développé. Dans un deuxième temps, un contrôleur par MGOS homogène universel est développé où il est montré que le degré d’homogénéité peut être manipulé pour obtenir des avantages supplémentaires, tels que la bornitude de la commande, la garantie d’une amplitude minimale de la discontinuité de la commande et la convergence en temps fixe. Les performances des contrôleurs proposés ont été démontrées par des simulations et à travers des résultats expérimentaux sur un système pile à combustible.Dans la deuxième partie de la thèse, deux problèmes de commande de systèmes sous-actionnés sont étudiés. Le premier problème concerne le suivi de chemin global d’un robot mobile avec un point de visée. Le deuxième problème concerne la poursuite de trajectoire globale d’un bateau. Ces deux problèmes sont de nature distincte, cependant, ils sont soumis à des contraintes physiques similaires liées à la bornitude de la commande. Ainsi, les contrôleurs proposés sont basés sur l’utilisation de commandes saturées. Des simulations ont été effectuées pour démontrer les performances de ces contrôleurs. / Nonlinear systems are so diverse that generalized tools for control are difficult to develop. Nonlinear control theory requires rigorous mathematical analysis to justify its conclusions. This thesis addresses two distinct, yet important branches of nonlinear control theory: control of uncertain nonlinear systems and control of under-actuated systems.In the first part, a class of Lyapunov-based robust arbitrary higher order sliding mode (HOSM) controllers is developed for the control of uncertain nonlinear systems. This class of controllers is based on a class of controllers for finite-time stabilization of pure integrator chain, and requires the limits of the system uncertainty to be known a-priori. Then, in order to eliminate the dependence on the knowledge of these limits, an adaptive arbitrary HOSM controller is developed. Using this new class, a universal homogeneous arbitrary HOSM controller is developed and it is shown that the homogeneity degree can be manipulated to obtain additional advantages in the proposed controllers, such as bounded control, minimum amplitude of discontinuous control and fixed time convergence. The performance of the controllers has been demonstrated through simulations and experiments on a fuel cell system.In the next part, the control of two under-actuated systems is studied. The first control problem is the global path following of car-type robotic vehicle, using target-point. The second problem is the precise tracking of surface marine vessels. Both these problems are distinct in nature; however, they are subjected to similar physical constraints. The solutions proposed for these control problems use saturated controls, taking into account the physical bounds on the control inputs. Simulations have been performed to demonstrate the performance of these controllers.

This research proposes a novel approach for improving autonomous mobile system navigation in dynamic and potentially occluded environments. The research introduces a tracking framework that combines data from stationary sensing units and on-board sensors, addressing challenges of computational efficiency, reliability, and scalability. The work innovates by integrating spatially-distributed LiDAR and RGB-D Camera sensors, with the optional inclusion of on-board IMU-based dead-reckoning, forming a robust and efficient coordination framework for autonomous systems. Two key developments are achieved. Firstly, a point cloud object detection technique, "Generalized L-Shape Fitting”, is advanced, enhancing bounding box fitting over point cloud data. Secondly, a new estimation framework, the Distributed Edge Node Switching Filter (DENS-F), is established. The DENS-F optimizes resource utilization and coordination, while minimizing reliance on on-board computation. Furthermore, it incorporates a short-term predictive feature, thanks to the Adaptive-Constant Acceleration motion model, which utilizes behaviour-based control inputs. The findings indicate that the DENS-F substantially improves accuracy and computational efficiency compared to the Kalman Consensus Filter (KCF), particularly when additional inertial data is provided by the vehicle. The type of sensor deployed and the consistency of the vehicle's path are also found to significantly influence the system's performance. The research opens new viewpoints for enhancing autonomous vehicle tracking, highlighting opportunities for future exploration in prediction models, sensor selection, and precision coordination. / Denna forskning föreslår en ny metod för att förbättra autonom mobil systemsnavigering i dynamiska och potentiellt skymda miljöer. Forskningen introducerar ett spårningsramverk som kombinerar data från stationära sensorenheter och ombordssensorer, vilket hanterar utmaningar med beräkningsefektivitet, tillförlitlighet och skalbarhet. Arbetet innoverar genom att integrera spatialt distribuerade LiDAR- och RGB-D-kamerasensorer, med det valfria tillägget av ombord IMU-baserad dödräkning, vilket skapar ett robust och efektivt samordningsramverk för autonoma system. Två nyckelutvecklingar uppnås. För det första avanceras en punktmolnsobjektdetekteringsteknik, “Generaliserad L-formig anpassning”, vilket förbättrar anpassning av inneslutande rutor över punktmolnsdata. För det andra upprättas ett nytt uppskattningssystem, det distribuerade kantnodväxlingsfltret (DENSF). DENS-F optimerar resursanvändning och samordning, samtidigt som det minimerar beroendet av ombordberäkning. Vidare införlivar det en kortsiktig prediktiv funktion, tack vare den adaptiva konstanta accelerationsrörelsemodellen, som använder beteendebaserade styrentréer. Resultaten visar att DENS-F väsentligt förbättrar noggrannhet och beräknings-efektivitet jämfört med Kalman Consensus Filter (KCF), särskilt när ytterligare tröghetsdata tillhandahålls av fordonet. Den typ av sensor som används och fordonets färdvägs konsekvens påverkar också systemets prestanda avsevärt. Forskningen öppnar nya synvinklar för att förbättra spårning av autonoma fordon, och lyfter fram möjligheter för framtida utforskning inom förutsägelsemodeller, sensorval och precisionskoordinering. / Questa ricerca propone un nuovo approccio per migliorare la navigazione dei sistemi mobili autonomi in ambienti dinamici e potenzialmente ostruiti. La ricerca introduce un sistema di tracciamento che combina dati da unità di rilevazione stazionarie e sensori di bordo, afrontando le sfde dell’effcienza computazionale, dell’affdabilità e della scalabilità. Il lavoro innova integrando sensori LiDAR e telecamere RGB-D distribuiti nello spazio, con l’inclusione opzionale di una navigazione inerziale basata su IMU di bordo, formando un robusto ed effciente quadro di coordinamento per i sistemi autonomi. Vengono raggiunti due sviluppi chiave. In primo luogo, viene perfezionata una tecnica di rilevazione di oggetti a nuvola di punti, “Generalized L-Shape Fitting”, migliorando l’adattamento del riquadro di delimitazione sui dati della nuvola di punti. In secondo luogo, viene istituito un nuovo framework di stima, il Distributed Edge Node Switching Filter (DENS-F). Il DENS-F ottimizza l’utilizzo delle risorse e il coordinamento, riducendo al minimo la dipendenza dal calcolo di bordo. Inoltre, incorpora una caratteristica di previsione a breve termine, grazie al modello di movimento Adaptive-Constant Acceleration, che utilizza input di controllo basati sul comportamento del veicolo. I risultati indicano che il DENS-F migliora notevolmente l’accuratezza e l’effcienza computazionale rispetto al Kalman Consensus Filter (KCF), in particolare quando il veicolo fornisce dati inerziali aggiuntivi. Si scopre anche che il tipo di sensore impiegato e la coerenza del percorso del veicolo infuenzano signifcativamente le prestazioni del sistema. La ricerca apre nuovi punti di vista per migliorare il tracciamento dei veicoli autonomi, evidenziando opportunità per future esplorazioni nei modelli di previsione, nella selezione dei sensori e nel coordinamento di precisione.