Geometric Adaptive Control for a Quadrotor UAV with Wind Disturbance Rejection
For quadrotor UAV control, this work provides a theoretically grounded adaptive method to handle unstructured wind disturbances, though results are only simulated.
This paper develops a geometric adaptive controller for quadrotor UAVs that uses an online-adjusted multilayer neural network to reject unknown wind disturbances. Lyapunov analysis proves uniformly ultimately bounded tracking errors, and simulations demonstrate successful disturbance rejection.
This paper presents a geometric adaptive control scheme for a quadrotor unmanned aerial vehicle, where the effects of unknown, unstructured disturbances are mitigated by a multilayer neural network that is adjusted online. The stability of the proposed controller is analyzed with Lyapunov stability theory on the special Euclidean group, and it is shown that the tracking errors are uniformly ultimately bounded with an ultimate bound that can be abridged arbitrarily. A mathematical model of wind disturbance on the quadrotor dynamics is presented, and it is shown that the proposed adaptive controller is capable of rejecting the effects of wind disturbances successfully. These are illustrated by numerical examples.