TY - GEN
T1 - Data-Driven Modeling for Multirotor Autonomous Control
AU - Shiotsuka, Tatsuya
AU - Bando, Mai
AU - Hokamoto, Shinji
N1 - Publisher Copyright:
© 2024 by Tatsuya Shiotsuka. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85192359031&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85192359031&partnerID=8YFLogxK
U2 - 10.2514/6.2024-0568
DO - 10.2514/6.2024-0568
M3 - Conference contribution
AN - SCOPUS:85192359031
SN - 9781624107115
T3 - AIAA SciTech Forum and Exposition, 2024
BT - AIAA SciTech Forum and Exposition, 2024
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA SciTech Forum and Exposition, 2024
Y2 - 8 January 2024 through 12 January 2024
ER -