Data-Driven Modeling for Multirotor Autonomous Control

Tatsuya Shiotsuka, Mai Bando, Shinji Hokamoto

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

The precise trajectory control of UAVs, including multirotors, is challenging due to uncertainties such as modeling errors, unmodeled aerodynamic forces, and wind disturbances. To address this issue, this paper focuses on real-time modeling of quadrotor motion exploiting recently developed data-driven methods. By constructing a dynamical model in real-time using data collected during flight, it is expected to develop a dynamical model that incorporates uncertainties arising from modeling errors and disturbances. In particular, the data-driven modeling method called Real-Time Update Dynamic Mode Decomposition (RTDMD), an extension of the DMD algorithm with sequential update rules, is employed for the real-time modeling of dynamical models. Identification experiments using experimental data demonstrate that the RTDMD successfully obtained a dynamical model for estimating the quadrotor’s state.

本文言語英語
ホスト出版物のタイトルAIAA SciTech Forum and Exposition, 2024
出版社American Institute of Aeronautics and Astronautics Inc, AIAA
ISBN(印刷版)9781624107115
DOI
出版ステータス出版済み - 2024
イベントAIAA SciTech Forum and Exposition, 2024 - Orlando, 米国
継続期間: 1月 8 20241月 12 2024

出版物シリーズ

名前AIAA SciTech Forum and Exposition, 2024

会議

会議AIAA SciTech Forum and Exposition, 2024
国/地域米国
CityOrlando
Period1/8/241/12/24

!!!All Science Journal Classification (ASJC) codes

  • 航空宇宙工学

フィンガープリント

「Data-Driven Modeling for Multirotor Autonomous Control」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル