Journal
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Volume 29, Issue 4, Pages 1533-1548Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2020.3006184
Keywords
Aerodynamics; Neural networks; Rotors; Wind speed; Adaptive control; Control systems; Adaptive control; geometric control; neural network; quadrotor unmanned aerial vehicles (UAVs); wind disturbance rejection
Funding
- NSF [CNS-1837382]
- Air Force Office of Scientific Research (AFOSR) [FA9550-18-1-0288]
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This article presents a geometric adaptive controller for a quadrotor unmanned aerial vehicle with artificial neural networks. By adjusting the weights of the neural networks online, the controller aims to mitigate the effects of unknown disturbances and achieve successful tracking control in position and heading directions.
This article presents a geometric adaptive controller for a quadrotor unmanned aerial vehicle with artificial neural networks. It is assumed that the dynamics of a quadrotor is disturbed by the arbitrary, unstructured forces and moments caused by wind. To address this, the proposed control system is augmented with the multilayer neural networks, and the weights of the neural networks are adjusted online according to an adaptive law. By using the universal approximation theorem, it is shown that the effects of the unknown disturbances can be mitigated. More specifically, under the proposed control system, the tracking errors in the position and heading directions are uniformly ultimately bounded. These are developed directly on the special Euclidean group to avoid the complexities or singularities inherent to local parameterizations. The efficacy of the proposed control system is first illustrated by numerical examples. Then, several indoor flight experiments are presented to demonstrate that the proposed controller successfully rejects the effects of wind disturbances even for aggressive, agile maneuvers.
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