Geometric Adaptive Control With Neural Networks for a Quadrotor in Wind Fields

Abstract

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.