Atapattu, Sachithra (Memorial University of Newfoundland), De Silva, Oscar (Memorial University of Newfoundland), Wanasinghe, Thumeera R. (Memorial University of Newfoundland), Mann, George Kingsly (Memorial University of Newfoundland), Gosine, Ray (C-CORE / Memorial University of Newfoundland)

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

Scheduled for presentation during the Regular Session "Unmanned aerial vehicles" (FrBT11), Friday, September 27, 2024, 13:50−14:10, Salon 19

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

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.