In this article we propose a hierarchical control structure for multi-agent systems. The main objective is to perform formation change manoeuvres, with guaranteed safe distance between each two vehicles throughout the whole mission.The key components that ensure safety are a robust control algorithm that is capable of stabilising the group of vehicles in a desired formation and a higher level path generation method that provides all the vehicles with safe paths, based on graph theoretic considerations. The method can efficiently handle a large group of any type of vehicles. As an illustration, the results are applied to a group of quadrotor UAVs.
Abstract-The design and the initial realization of control on an experimental in-door unmanned autonomous quadrotor helicopter is presented. This is a hierarchical embedded modelbased control scheme that is built upon the concept of backstepping, and is applied on an electric motor-driven quadrotor UAV hardware that is equipped with an embedded on-board computer, inertial sensor unit, as well as facilities that make it suitable to be involved in an in-door positioning system, and wireless digital communication network. This realization forms an important step in the development process of a more advanced realization of an UAV suitable for practical applications; it aims clarification of the control principles, acquiring experience in solving control tasks, and getting skills for the development of further realizations.
The article focuses on different aspects (both theoretical and practical) of the development of the control algorithm of a quadrotor helicopter starting from the modelling phase. A new control algorithm is elaborated and supplementary components are described in detail including state estimation and path tracking. The helicopter's dynamic model takes into account the aerodynamic friction, the gyroscopic effect of the rotors and also the motor dynamics. The control algorithm is based on the backstepping approach and is capable of stabilising the model even in case of realistic noises. Vision system and on-board inertial measurement unit provide the measurements and two-level extended Kalman filter based state estimator is used to suppress the measurement noises and to estimate the unmeasured signals. The methods of the software development and real-time testing are also presented with attention to the sources of common errors.
The paper deals with the quick prototype design and hardware-in-the-loop real-
IntroductionIncreasing attention has been focused on the problem of controlling large scale systems that are built up from several smaller subsystems, e.g. a group of UAVs. Controlling a group of vehicles together can result in better overall performance and certain tasks can also be performed more effectively. Examples to such cases are surveillance missions, fuel consumption reduction by travelling in formation.Advances in communication technology, miniaturisation and increased computation power open the way to implement not only local, but also formation level control algorithms on board of a single vehicle. Performing all the required calculations in a centralised manner is often not viable. In such cases, distributed solutions are required, even though additional problems arise, e.g. communication errors or delays.Several methods have been elaborated that solve certain problems related to multi-vehicle systems. Each of them have strengths and weaknesses, thus they have evolved in parallel. Two of the most frequently applied methods are the model predictive control (MPC) and robust control techniques.Obstacle and collision avoidance is most often solved by applying MPC methods [3,7,11,12,16]. MPC involves numerical optimisation (occasionally mixed integer programming) at every single time instant and it is a flexible framework, various objectives can be included into the problem formulation. The cost is the increased computational complexity that may require more computational power than what currently exists.Other approaches include robust control methods [5,6,8,10,17] that can guarantee certain types of robustness and performance but cannot handle hard constraints the way MPC can. This is the motivation of the method we propose in the following. A promising formation stabilising algorithm is presented in [8], which ensures that vehicles reach a desired formation, even if the communication topology changes arbitrarily and arbitrarily quickly. It utilises the graph theoretical results of [2]. However, it does not guarantee that vehicles do not collide with each other during the transients. We extend this approach by a higher level method effectively which tackles the above problem, even for a relatively large group of vehicles.
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