Improving the Region of Attraction of a Multi-rotor UAV by Estimating Unknown Disturbances

TL;DR

This paper introduces a machine learning-based method to improve the accuracy of estimating the region of attraction for a multi-rotor UAV by predicting unknown disturbances and integrating them into the model.

Contribution

It presents a neural network approach to estimate unknown disturbances, enhancing ROA calculation accuracy over traditional Lyapunov-based methods.

Findings

The proposed method yields a more accurate ROA estimation.

It outperforms conventional Lyapunov-based approaches in conservativeness.

The neural network effectively predicts unknown disturbances.

Abstract

This study presents a machine learning-aided approach to accurately estimate the region of attraction (ROA) of a multi-rotor unmanned aerial vehicle (UAV) controlled using a linear quadratic regulator (LQR) controller. Conventional ROA estimation approaches rely on a nominal dynamic model for ROA calculation, leading to inaccurate estimation due to unknown dynamics and disturbances associated with the physical system. To address this issue, our study utilizes a neural network to predict these unknown disturbances of a planar quadrotor. The nominal model integrated with the learned disturbances is then employed to calculate the ROA of the planer quadrotor using a graphical technique. The estimated ROA is then compared with the ROA calculated using Lyapunov analysis and the graphical approach without incorporating the learned disturbances. The results illustrated that the proposed method provides a more accurate estimation of the ROA, while the conventional Lyapunov-based estimation tends to be more conservative.