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Reinforcement learning-based approximate optimal control for attitude reorientation under state constraints
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Dong, Hongyang, Zhao, Xiaowei and Yang, Haoyang (2021) Reinforcement learning-based approximate optimal control for attitude reorientation under state constraints. IEEE Transactions on Control Systems Technology, 29 (4). pp. 1664-1673. doi:10.1109/TCST.2020.3007401 ISSN 1063-6536.
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Official URL: https://doi.org/10.1109/TCST.2020.3007401
Abstract
This paper addresses the attitude reorientation problems of rigid bodies under multiple state constraints. A novel reinforcement learning (RL)-based approximate optimal control method is proposed to make the trade-off between control cost and performance. The novelty lies in that it guarantees constraint handling abilities on attitude forbidden zones and angular-velocity limits. To achieve this, barrier functions are employed to encode the constraint information into the cost function. Then an RL-based learning strategy is developed to approximate the optimal cost function and control policy. A simplified critic-only neural network (NN) is employed to replace the conventional actor-critic structure once adequate data is collected online. This design guarantees the uniform boundedness of reorientation errors and NN weight estimation errors subject to the satisfaction of a finite excitation condition, which is a relaxation compared with the persistent excitation condition that is typically required for this class of problems. More importantly, all underlying state constraints are strictly obeyed during the online learning process. The effectiveness and advantages of the proposed controller are verified by both numerical simulations and experimental tests based on a comprehensive hardware-in-loop testbed.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
| Library of Congress Subject Headings (LCSH): | Aerospace engineering, Control theory, Dynamic programming , Adaptive control systems | ||||||||
| Journal or Publication Title: | IEEE Transactions on Control Systems Technology | ||||||||
| Publisher: | IEEE | ||||||||
| ISSN: | 1063-6536 | ||||||||
| Official Date: | July 2021 | ||||||||
| Dates: |
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| Volume: | 29 | ||||||||
| Number: | 4 | ||||||||
| Page Range: | pp. 1664-1673 | ||||||||
| DOI: | 10.1109/TCST.2020.3007401 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Reuse Statement (publisher, data, author rights): | © 2020 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: | 9 July 2020 | ||||||||
| Date of first compliant Open Access: | 21 July 2020 | ||||||||
| RIOXX Funder/Project Grant: |
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| Is Part Of: | 1 |
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