TY - JOUR
T1 - Attitude Control using Iterative Learning Control Considering Orbital Motion of NRHO
AU - Hayashi, Yuta
AU - Bando, Mai
AU - Hokamoto, Shinji
N1 - Publisher Copyright:
Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.
PY - 2023
Y1 - 2023
N2 - Recently, deep space exploration has advanced particularly with orbital characteristics of NRHO being widely studied. However, studies for spacecraft autonomy have not progressed well. A control law that combines feedback control and Iterative Learning Control(ILC) is applied to the attitude control system of a spacecraft, and furthermore, the control input is realized by a CMG capable of high output, with the aim of improving the attitude tracking control system step by step by increasing the orbital motion.
AB - Recently, deep space exploration has advanced particularly with orbital characteristics of NRHO being widely studied. However, studies for spacecraft autonomy have not progressed well. A control law that combines feedback control and Iterative Learning Control(ILC) is applied to the attitude control system of a spacecraft, and furthermore, the control input is realized by a CMG capable of high output, with the aim of improving the attitude tracking control system step by step by increasing the orbital motion.
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M3 - Conference article
AN - SCOPUS:85187994581
SN - 0074-1795
VL - 2023-October
JO - Proceedings of the International Astronautical Congress, IAC
JF - Proceedings of the International Astronautical Congress, IAC
T2 - 74th International Astronautical Congress, IAC 2023
Y2 - 2 October 2023 through 6 October 2023
ER -