Geometric Adaptive Control for a Quadrotor UAV with Wind Disturbance Rejection

TL;DR

This paper introduces a geometric adaptive control method for quadrotors that effectively mitigates wind disturbances using an online-adjusted neural network, ensuring stability and bounded tracking errors.

Contribution

It proposes a novel geometric adaptive control scheme with neural network adaptation for quadrotors, providing stability analysis and disturbance rejection capabilities.

Findings

Successfully rejects wind disturbances in simulations

Ensures bounded tracking errors with adjustable bounds

Provides Lyapunov stability proof on Euclidean group

Abstract

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.