Sample efficient deep reinforcement learning for Diwata microsatellite reaction wheel attitude control-Reference-Cited by-同舟云学术

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Sample efficient deep reinforcement learning for Diwata microsatellite reaction wheel attitude control

Published:2022-10-29 Issue:1 Volume:6 Page:61-69
ISSN:2523-3947
Container-title:Aerospace Systems
language:en
Short-container-title:AS

Author:

Sarmiento James-Andrew R.,Tan Vanessa H.,Talampas Marc Caesar R.,Naval Prospero C.

Publisher

Springer Science and Business Media LLC

Subject

Space and Planetary Science,Computers in Earth Sciences,Mechanical Engineering,Social Sciences (miscellaneous),Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42401-022-00169-3.pdf

Reference32 articles.

1. Diwata-1—Satellite Missions—eoPortal Directory (2021) https://directory.eoportal.org/web/eoportal/satellite-missions/d/diwata-1. Accessed 25 June 2021

2. Yang C-D, Sun Y-P (2002) Mixed h2/$$h\infty $$ state-feedback design for microsatellite attitude control. Control Eng Pract 10(9):951–970

3. Su J, Cai K-Y (2011) Globally stabilizing proportional-integral-derivative control laws for rigid-body attitude tracking. J Guid Control Dyn 34(4):1260–1264

4. Paynter SJ, Bishop RH (1997) Adaptive nonlinear attitude control and momentum management of spacecraft. J Guid Control Dyn 20(5):1025–1032

5. Fragopoulos D, Innocenti M (2004) Stability considerations in quaternion attitude control using discontinuous lyapunov functions. IEE Proc Control Theory Appl 151(3):253–258

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