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

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.