Online Aerodynamic Model Identification on Small Fixed-Wing UAVs with Uncertain Flight Data

Paris Vaiopoulos, Georgios Zogopoulos-Papaliakos, Kostas J. Kyriakopoulos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6587-6592
Number of pages6
ISBN (Electronic)9781538630815
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Robotics and Automation, ICRA 2018 - Brisbane, Australia
Duration: May 21 2018May 25 2018

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Country/TerritoryAustralia
CityBrisbane
Period5/21/185/25/18

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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