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Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances

MPG-Autoren
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Liu,  Y
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Rajappa,  S
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Bülthoff,  HH
Project group: Cybernetics Approach to Perception & Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Liu, Y., Rajappa, S., Montenbruck, J., Stegagno, P., Bülthoff, H., Allgöwer, F., et al. (2017). Robust nonlinear control approach to nontrivial maneuvers and obstacle avoidance for quadrotor UAV under disturbances. Robotics and Autonomous Systems, 98, 317-332. doi:10.1016/j.robot.2017.08.011.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-C272-4
Zusammenfassung
In this paper, we present an onboard robust nonlinear control approach for quadrotor Unmanned Aerial Vehicles (UAVs) in the environments with disturbances and obstacles. The complete framework consists of an attitude controller based on the solution of global output regulation problems for SO(), a backstepping-like position controller, a -dimensional wrench observer to estimate the unknown force and torque disturbances, and an online trajectory planner based on a model predictive control method with obstacle avoiding constraints. We prove the strong convergence properties of the proposed method both in theory and via real-robot experiments. The control approach is onboard implemented on a quadrotor UAV, and has been validated through intensive experiments and compared with other nonlinear control methods for waypoint navigation and large-tilted path following tasks in the presence of external disturbances, e.g. wind gusts. The presented approach has also been evaluated in the scenarios with randomly located obstacles.