Online Aerodynamic Model Identification on Small Fixed-Wing UAVs with Uncertain Flight Data
Paris Vaiopoulos, Georgios Zogopoulos-Papaliakos, Kostas J. Kyriakopoulos
Abstract
This paper focuses on real-time estimation of the aerodynamic model parameters of small-scale fixed wing Unmanned Aerial Vehicles (UAVs) without the aid of wind-tunnel experiments, using exclusively flight data. The key tool of the following analysis centers around the principles of Total Least Squares estimation. Contrary to Ordinary Least Squares, this method accounts for errors in both explanatory data and variables to-be-explained. This is a highly desirable property for UAVs equipped with low-cost sensor systems. The proposed implementation combines both batch and real-time schemes, while deals efficiently with the problem of Insufficient System Excitation. Online adaptation to model changes is performed by applying a Variable Forgetting Factor to the estimation data. Finally, a Monte Carlo approach is developed for uncertainty estimation regarding compound aerodynamic variables.
BibTeX
@inproceedings{icra2018_onlineaerodynami,
title = {Online Aerodynamic Model Identification on Small Fixed-Wing UAVs with Uncertain Flight Data},
author = {Paris Vaiopoulos and Georgios Zogopoulos-Papaliakos and Kostas J. Kyriakopoulos},
booktitle = {ICRA 2018},
year = {2018}
}