Geometric Adaptive Control with Neural Networks for a Quadrotor UAV in Wind fields
For quadrotor UAV control, this work addresses the problem of unknown wind disturbances with a theoretically grounded adaptive neural network approach that avoids singularities of local parameterizations.
The paper proposes a geometric adaptive controller augmented with neural networks for quadrotor UAVs to mitigate arbitrary wind disturbances. The controller achieves uniformly ultimately bounded tracking errors with arbitrarily reducible bounds, demonstrated through numerical simulations and indoor flight experiments including aggressive maneuvers.
This paper proposes 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 arbitrary, unstructured forces and moments caused by wind. To address this, the proposed control system is augmented with multilayer neural networks, and the weights of neural networks are adjusted online according to an adaptive law. By utilizing the universal approximation theorem, it is shown that the effects of unknown disturbances can be mitigated. More specifically, under the proposed control system, the tracking errors in the position and the heading direction are uniformly ultimately bounded where the ultimate bound can be reduced arbitrarily. These are developed directly on the special Euclidean group to avoid 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.