Modern aerospace vehicles are expected to have nonconventional flight envelopes and, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Networks (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper we propose a real-time system, based on a NN model, in order to control the trajectories of a hexacopter. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and obtained coordinates of the hexacopter.

A Real-Time System based on a Neural Network Model to Control Hexacopter Trajectories

COLLOTTA, MARIO;PAU, GIOVANNI
2014

Abstract

Modern aerospace vehicles are expected to have nonconventional flight envelopes and, in order to operate in uncertain environments, they must guarantee a high level of robustness and adaptability. A Neural Networks (NN) controller, with real-time learning capability, can be used in applications with manned or unmanned aerial vehicles. In this paper we propose a real-time system, based on a NN model, in order to control the trajectories of a hexacopter. The paper shows a performance evaluation, through a real experimental testbed, of the proposed approach in terms of error measures and obtained coordinates of the hexacopter.
978-147994749-2
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11387/52327
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