This paper presents a neural network based model reference adaptive controller (MRAC) for the control of a fixed-wing unmanned aerial vehicle (UAV). An adaptive neural network is trained using the error between the UAV response and the desired response as given by the reference model. The design of a suitable reference model for the desired aircraft performance is investigated and developed. Unknown nonlinearities of the vehicle dynamics not accounted by the reference model are compensated in real-time by the adaptive neural network approximation, which also provides online adaptation during off-nominal flight conditions. The nonlinear dynamics of a twin-engine UAV are modeled in Simulink to test the controller in a software-in-the-loop simulation environment. Simulation results of maneuvers show the feasibility and performance of the proposed controller.
Neural network based nonlinear model reference adaptive controller for an unmanned aerial vehicle
01.06.2017
1045690 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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