Automatic Gain Tuning Method of a Quad-Rotor Geometric Attitude Controller Using A3C

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In this paper, we address an automated gain tuning method using Asynchronous Advantage Actor-Critic (A3C) reinforcement learning approach. A quad-rotor Unmanned Aerial Vehicle (UAV) with nonlinear geometric tracking controller is introduced to test our approach. In the geometric controller, two attitude gains must be provided appropriately to achieve stable error dynamics. To ease the difficulties while optimizing the controller performances, such as minimizing tracking error together with reducing control energy, we made Reinforcement Learning (RL) agents to substitute the entire gain tuning process. By training the RL agents with multiple quad-rotor configurations, we were not only able to reduce our efforts putting into the gain tuning by the trial-and-error methods, but also able to deal with the parameter changes by constructing an adaptive structure.
Publisher
SPRINGER
Issue Date
2020-06
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES, v.21, no.2, pp.469 - 478

ISSN
2093-274X
DOI
10.1007/s42405-019-00233-x
URI
http://hdl.handle.net/10203/279540
Appears in Collection
AE-Journal Papers(저널논문)
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