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Learning-based 6-DOF control for autonomous proximity operations under motion constraints
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Qinglei, Hu, Haoyang, Yang, Hongyang, Dong and Xiaowei, Zhao (2021) Learning-based 6-DOF control for autonomous proximity operations under motion constraints. IEEE Transactions on Aerospace and Electronic Systems, 57 (6). 4097 -4109. doi:10.1109/TAES.2021.3094628 ISSN 0018-9251.
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WRAP-Learning-based-6-DOF-autonomous-proximity-operations-motion-constraints-2021.pdf - Accepted Version - Requires a PDF viewer. Download (3896Kb) | Preview |
Official URL: https://doi.org/10.1109/TAES.2021.3094628
Abstract
This paper proposes a reinforcement learning (RL)-based six-degree-of-freedom (6-DOF) control scheme for the final phase proximity operations of spacecraft. The main novelty of the proposed method are from two aspects: 1) the closed-loop performance can be improved in real-time through the RL technique, achieving an online approximate optimal control subject to the full 6-DOF nonlinear dynamics of spacecraft; 2) Nontrivial motion constraints of proximity operations are considered and strictly obeyed during the whole control process. As a stepping stone, the dual-quaternion formalism is employed to characterize the 6-DOF dynamics model and motion constraints. Then, an RL-based control scheme is developed under the dual-quaternion algebraic framework to approximate the optimal control solution subject to a cost function and a Hamilton-Jacobi-Bellman equation. In addition, a specially designed barrier function is embedded in the reward function to avoid motion constraint violations. The Lyapunov-based stability analysis guarantees the ultimate boundedness of state errors and the weight of NN estimation errors. Besides, we also show that a PD-like controller under dual-quaternion formulation can be employed as the initial control policy to trigger the online learning process. The boundedness of it is proved by a special Lyapunov strictification method. Simulation results of prototypical spacecraft missions with proximity operations are provided to illustrate the effectiveness of the proposed method.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | Q Science > Q Science (General) T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Automatic control , Reinforcement learning , Dynamic programming | |||||||||||||||
Journal or Publication Title: | IEEE Transactions on Aerospace and Electronic Systems | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISSN: | 0018-9251 | |||||||||||||||
Official Date: | December 2021 | |||||||||||||||
Dates: |
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Volume: | 57 | |||||||||||||||
Number: | 6 | |||||||||||||||
Page Range: | 4097 -4109 | |||||||||||||||
DOI: | 10.1109/TAES.2021.3094628 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 3 July 2021 | |||||||||||||||
Date of first compliant Open Access: | 7 July 2021 | |||||||||||||||
RIOXX Funder/Project Grant: |
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