A prototype of an autonomous controller for a quadrotor UAV

The paper proposes a complete real-time control algorithm for autonomous collision-free operations of the quadrotor UAV. As opposed to fixed wing vehicles the quadrotor is a small agile vehicle which might be more suitable for the variety of specific applications including search and rescue, surveillance and remote inspection. The developed control system incorporates both trajectory planning and path following. Using a differential flatness property the trajectory planning is posed as a constrained optimization problem in the output space (as opposed to the control space), which simplifies the problem. The trajectory and speed profile are parameterized to reduce the problem to a finite dimensional problem. To optimize the speed profile independently of the trajectory a virtual argument is used as opposed to time. A path following portion of the proposed algorithm uses a standard linear multi-variable control technique. The paper presents the results of simulations to demonstrate the suitability of the proposed control algorithm

A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications

Motion Coordination of Aerial Vehicles

The coordinated motion control of multiple vehicles has emerged as a field of major interest in the control community. This thesis addresses two topics related to the control of a group of aerial vehicles: the output feedback attitude synchronization of rigid bodies and the formation control of Unmanned Aerial Vehicles (UAVs) capable of Vertical Take-Off and Landing (VTOL). The information flow between members of the team is assumed fixed and undirected. The first part of this thesis is devoted to the attitude synchronization of a group of spacecraft. In this context, we propose control schemes for the synchronization of a group of spacecraft to a predefined attitude trajectory without angular velocity measurements. We also propose some velocity-free consensus-seeking schemes allowing a group of spacecraft to align their attitudes, without reference trajectory specification. The second part of this thesis is devoted to the control of a group of VTOL-UAVs in the Special Euclidian group SE(3), i.e., position and orientation. In this context, we propose a few position coordination schemes without linear-velocity measurements. We also propose some solutions to the same problem in the presence of communication time-delays between aircraft. To solve the above mentioned problems, several new technical tools have been introduced in this thesis to overcome the deficiencies of the existing techniques in this field

Towards autonomy of a quadrotor UAV

As the potential of unmanned aerial vehicles rapidly increases, there is a growing interest in rotary vehicles as well as fixed wing. The quadrotor is small agile rotary vehicle controlled by variable speed prop rotors. With no need for a swash plate the vehicle is low cost as well as dynamically simple. In order to achieve autonomous flight, any potential control algorithm must include trajectory generation and trajectory following. Trajectory generation can be done using direct or indirect methods. Indirect methods provide an optimal solution but are hard to solve for anything other than the simplest of cases. Direct methods in comparison are often sub-optimal but can be applied to a wider range of problems. Trajectory optimization is typically performed within the control space, however, by posing the problem in the output space, the problem can be simplified. Differential flatness is a property of some dynamical systems which allows dynamic inversion and hence, output space optimization. Trajectory following can be achieved through any number of linear control techniques, this is demonstrated whereby a single trajectory is followed using LQR, this scheme is limited however, as the vehicle is unable to adapt to environmental changes. Model based predictive control guarantees constraint satisfaction at every time step, this however is time consuming and therefore, a combined controller is proposed benefiting from the adaptable nature of MBPC and the robustness and simplicity of LQR control. There are numerous direct methods for trajectory optimization both in the output and control space. Taranenko’s direct method has a number of benefits over other techniques, including the use of a virtual argument, which separates the optimal path and the speed problem. This enables the algorithm to solve the optimal time problem, the optimal fuel problem or a combination of the two, without a deviation from the optimal path. In order to implement such a control scheme, the issues of feedback, communication and control action computation, require consideration. This work discusses the issues with instrumentation and communication encountered when developing the control system and provides open loop test results. This work also extends the proposed control schemes to consider the problem of multiple vehicle flight rendezvous. Specifically the problem of rendezvous when there is no communication link, limited visibility and no agreed rendezvous point. Using Taranenko’s direct method multiple vehicle rendezvous is simulated.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Commande référencée vision pour drones à décollages et atterrissages verticaux

La miniaturisation des calculateurs a permis le développement des drones, engins volants capable de se déplacer de façon autonome et de rendre des services, comme se rendre clans des lieux peu accessibles ou remplacer l'homme dans des missions pénibles. Un enjeu essentiel dans ce cadre est celui de l'information qu'ils doivent utiliser pour se déplacer, et donc des capteurs à exploiter pour obtenir cette information. Or nombre de ces capteurs présentent des inconvénients (risques de brouillage ou de masquage en particulier). L'utilisation d'une caméra vidéo dans ce contexte offre une perspective intéressante. L'objet de cette thèse était l'étude de l'utilisation d'une telle caméra dans un contexte capteur minimaliste: essentiellement l'utilisation des données visuelles et inertielles. Elle a porté sur le développement de lois de commande offrant au système ainsi bouclé des propriétés de stabilité et de robustesse. En particulier, une des difficultés majeures abordées vient de la connaissance très limitée de l'environnement dans lequel le drone évolue. La thèse a tout d'abord étudié le problème de stabilisation du drone sous l'hypothèse de petits déplacements (hypothèse de linéarité). Dans un second temps, on a montré comment relâcher l'hypothèse de petits déplacements via la synthèse de commandes non linéaires. Le cas du suivi de trajectoire a ensuite été considéré, en s'appuyant sur la définition d'un cadre générique de mesure d'erreur de position par rapport à un point de référence inconnu. Enfin, la validation expérimentale de ces résultats a été entamée pendant la thèse, et a permis de valider bon nombre d'étapes et de défis associés à leur mise en œuvre en conditions réelles. La thèse se conclut par des perspectives pour poursuivre les travaux.The computers miniaturization has paved the way for the conception of Unmanned Aerial vehicles - "UAVs"- that is: flying vehicles embedding computers to make them partially or fully automated for such missions as e.g. cluttered environments exploration or replacement of humanly piloted vehicles for hazardous or painful missions. A key challenge for the design of such vehicles is that of the information they need to find in order to move, and, thus, the sensors to be used in order to get such information. A number of such sensors have flaws (e.g. the risk of being jammed). In this context, the use of a videocamera offers interesting prospectives. The goal of this PhD work was to study the use of such a videocamera in a minimal sensors setting: essentially the use of visual and inertial data. The work has been focused on the development of control laws offering the closed loop system stability and robustness properties. In particular, one of the major difficulties we faced came from the limited knowledge of the UAV environment. First we have studied this question under a small displacements assumption (linearity assumption). A control law has been defined, which took performance criteria into account. Second, we have showed how the small displacements assumption could be given up through nonlinear control design. The case of a trajectory following has then been considered, with the use of a generic error vector modelling with respect to an unknown reference point. Finally, an experimental validation of this work has been started and helped validate a number of steps and challenges associated to real conditions experiments. The work was concluded with prospectives for future work.TOULOUSE-ISAE (315552318) / SudocSudocFranceF

AAS/GSFC 13th International Symposium on Space Flight Dynamics

This conference proceedings preprint includes papers and abstracts presented at the 13th International Symposium on Space Flight Dynamics. Cosponsored by American Astronautical Society and the Guidance, Navigation and Control Center of the Goddard Space Flight Center, this symposium featured technical papers on a wide range of issues related to orbit-attitude prediction, determination, and control; attitude sensor calibration; attitude dynamics; and mission design

Proceedings of the NASA Conference on Space Telerobotics, volume 1

The theme of the Conference was man-machine collaboration in space. Topics addressed include: redundant manipulators; man-machine systems; telerobot architecture; remote sensing and planning; navigation; neural networks; fundamental AI research; and reasoning under uncertainty