Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

Quadrotor team modeling and control for DLO transportation

94 p.Esta Tesis realiza una propuesta de un modelado dinámico para el transporte de sólidos lineales deformables (SLD) mediante un equipo de cuadricópteros. En este modelo intervienen tres factores: - Modelado dinámico del sólido lineal a transportar. - Modelo dinámico del cuadricóptero para que tenga en cuenta la dinámica pasiva y los efectos del SLD. - Estrategia de control para un transporte e ciente y robusto. Diferenciamos dos tareas principales: (a) lograr una con guración cuasiestacionaria de una distribución de carga equivalente a transportar entre todos los robots. (b) Ejecutar el transporte en un plano horizontal de todo el sistema. El transporte se realiza mediante una con guración de seguir al líder en columna, pero los cuadricópteros individualmente tienen que ser su cientemente robustos para afrontar todas las no-linealidades provocadas por la dinámica del SLD y perturbaciones externas, como el viento. Los controladores del cuadricóptero se han diseñado para asegurar la estabilidad del sistema y una rápida convergencia del sistema. Se han comparado y testeado estrategias de control en tiempo real y no-real para comprobar la bondad y capacidad de ajuste a las condiciones dinámicas cambiantes del sistema. También se ha estudiado la escalabilidad del sistema

Autonomous Navigation of Quadrotor Swarms

RÉSUMÉ La mise sur le marché de composants toujours plus performants et compétitifs en termes de coût, ainsi que le développement rapide des technologies de commande et de navigation en robotique, nous ont amenés à envisager le contrôle d’un large essaim de quadrirotors. Di-verses solutions impliquant des drones existent déjà pour différentes applications: inventaire forestier, gestion du littoral, suivi du trafic, etc. Parmi celles-ci, la recherche et le sauvetage en situation d’urgence représentent à nos yeux la possibilité la plus intéressante et constitue, de fait, la première motivation de notre travail. Par conséquent, une large revue de littérature sur la question est fournie. Ce travail se concentre sur le contrôle de l’essaim lui-même, et non sur l’application finale. Tout d’abord, un modèle mathématique de la dynamique du quadrirotor est présenté et plusieurs lois de commande numérique sont synthétisées. Ces dernières implémentent les modes de fonctionnement nécessaires aux algorithmes de navigation, à savoir : commande en vitesse, commande en position et commande en suivi. Ensuite, deux solutions originales et complémentaires de contrôle d’essaim sont proposées. D’une part, un algorithme d’essaimage pour la navigation extérieure est développé. Contrairement à la plupart des travaux trouvés dans la littérature, la solution proposée ici gère non seulement le maintien, mais aussi l’initialisation de la formation. Plus spécifiquement, un modèle de formation hexagonale est introduit. Ensuite, les places en formation sont attribuées de façon optimale à l’aide de l’algorithme hongrois. Enfin, les agents se déplacent jusqu’à la place qui leur est assignée tout en évitant les autres agents avec un algorithme de navigation inspiré du Artificial Potential Field. De plus, cette solution tient compte de contraintes de conception réalistes et a été intégrée avec succès dans un logiciel embarqué de quadrotor déjà existant et opérationnel. Les résultats de simulations Software-In-The-Loop sont fournis. D’autre part, une solution d’essaimage pour la navigation intérieure est étudiée. L’algorithme proposé implémente un certain nombre de comportements individuels simples, de sorte qu’un grand essaim peut suivre un meneur dans des environnements encombrés en se fiant uniquement aux informations locales. Des simulations préliminaires sont effectuées et les résultats montrent qu’il serait possible de faire fonctionner, conformément au besoin étudié, un essaim de cent quadrirotors avec l’algorithme proposé. En particulier, l’essaim est capable de suivre le meneur, de maintenir la connectivité, d’éviter les collisions entre agents, d’éviter les obstacles et même de se faufiler dans des espaces étroits.----------ABSTRACT The ever-growing hardware capabilities and the rapid development of robotic control and navigation technologies have led us to consider the control of an entire swarm of quadrotors. Drone-based solutions have been developed for different applications: forest inventory, coastal management, traÿc monitoring, etc... Among these, the Search And Rescue application represents for us a very promising field of application and constitutes the first motivation of our work. As a result, a wide literature review on the matter is provided. Nevertheless, this work focuses on the swarm control itself, and not on the end user application. First, a mathematical model of the quadrotor dynamics is presented and several digital control laws are designed. The latter provide operating modes useful for the navigation algorithms, namely: velocity control, position control and tracking control. Then, two original and complimentary swarming solutions are proposed. On the one hand, a swarming algorithm for outdoor navigation is developed. Unlike most of the works reviewed in the literature, our solution handles not only the maintenance but also the initialization of the formation. More specifically, an hexagonal formation pattern is intro-duced. Then, positions are optimally assigned using the Hungarian algorithm. Finally, the agents move to their assigned position while avoiding collisions with the other fleet members thanks to a navigation algorithm inspired from Artificial Potential Field. In addition, this solution accounts for realistic design constraints and was successfully integrated into already existing quadrotor onboard software. Software-In-The-Loop simulation results are provided. On the other hand, a swarming solution for indoor navigation is investigated. The proposed algorithm enforces a certain set of expected individual simple behaviors such that a large swarm can follow a leader through cluttered environments relying only on local information. Preliminary simulations are run and the results show that it is possible to operate a swarm of a hundred quadrotors with the proposed algorithm. In particular, the swarm is able to follow the leader, maintain connectivity, avoid collisions with the other agents, avoid obstacles, and even squeeze to pass through narrow spaces

Design of an UAV swarm

This master thesis tries to give an overview on the general aspects involved in the design of an UAV swarm. UAV swarms are continuoulsy gaining popularity amongst researchers and UAV manufacturers, since they allow greater success rates in task accomplishing with reduced times. Appart from this, multiple UAVs cooperating between them opens a new field of missions that can only be carried in this way. All the topics explained within this master thesis will explain all the agents involved in the design of an UAV swarm, from the communication protocols between them, navigation and trajectory analysis and task allocation

Optimal Control of Multiple Quadrotors for Transporting a Cable Suspended Payload

In this thesis, the main aim is to improve the flight control performance for a cable suspended payload with single and two quadrotors based on optimised control techniques. The study utilised optimal controllers, such as the Linear Quadratic Regulator LQR, the Iterative based LQR (ILQR), the Model Predictive Control MPC and the dynamic game controller to solve tracking control problems in terms of stabilisation, accuracy, constraints and collision avoidance. The LQR control was applied to the system as the first control method and compared with the classical Proportional-Derivative controller PD. It was used to achieve the load path tracking performance for single and two quadrotors with a cable slung load. The second controller was ILQR, which was developed based on the LQR control method to deal with the model nonlinearity. The MPC technique was also applied to the linearised nonlinear model LMPC of two quadrotors with a payload suspended by cables and compared with a nonlinear MPC (NMPC). Both MPC controllers LMPC and NMPC considered the constraints imposed on the system states and control inputs. The dynamic game control method was developed based on an incentive strategy for a leader-follower framework with the consideration of different optimal cost functions. It was applied to the linearised nonlinear model. Selecting these control techniques led to a number of achievements. Firstly, they improved the system performance in terms of achieving the system stability and reducing the steady-state errors. Secondly, the system parameter uncertainties were taken into consideration by utilising the ILQR controller. Thirdly, the MPC controllers guaranteed the handling of constraints and external disturbances in linear and nonlinear systems. Finally, avoiding collision between the leader and follower robots was achieved by applying the dynamic game controller. The controllers were tested in MATLAB simulation and verified for various desired predefined trajectories. In real experiments, these controllers were used as high-level controllers, which produce the optimised trajectory points. Then a low-level controller (PD controller) was used to follow the optimised trajectory points