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Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances

Yinyan Zhang Orcid Logo, Shuai Li Orcid Logo, Jian Weng Orcid Logo

IEEE Transactions on Cybernetics, Volume: 52, Issue: 8, Pages: 7453 - 7463

Swansea University Author: Shuai Li Orcid Logo

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DOI (Published version): 10.1109/tcyb.2020.3041368

Abstract

In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal co...

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Published in: IEEE Transactions on Cybernetics
ISSN: 2168-2267 2168-2275
Published: Institute of Electrical and Electronics Engineers (IEEE) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa56084
first_indexed 2021-01-20T09:34:37Z
last_indexed 2023-01-11T14:35:04Z
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spelling 2022-07-20T16:35:45.8718678 v2 56084 2021-01-20 Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2021-01-20 ACEM In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal control law is derived through the approximation of the integral-type quadratic performance index with respect to the tracking error, where the equivalent unknown parameters are generated online by an auxiliary system that can learn the dynamics of the controlled system. It is proved that the state differences between the auxiliary system and the corresponding controlled USV vessel are globally asymptotically convergent to zero. Besides, the approach theoretically guarantees asymptotic optimality of the performance index. The efficacy of the method is demonstrated via simulations based on the real parameters of an USV vessel. Journal Article IEEE Transactions on Cybernetics 52 8 7453 7463 Institute of Electrical and Electronics Engineers (IEEE) 2168-2267 2168-2275 1 8 2022 2022-08-01 10.1109/tcyb.2020.3041368 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University 2022-07-20T16:35:45.8718678 2021-01-20T09:31:19.9189369 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Yinyan Zhang 0000-0002-0463-0291 1 Shuai Li 0000-0001-8316-5289 2 Jian Weng 0000-0003-4067-8230 3 56084__19162__44f40e17b4a4465e9fb13cb9da5e37c9.pdf 56084.pdf 2021-01-25T09:25:19.1835835 Output 907416 application/pdf Accepted Manuscript true true eng http://creativecommons.org/licenses/by-nc-nd/4.0/
title Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
spellingShingle Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
Shuai Li
title_short Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
title_full Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
title_fullStr Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
title_full_unstemmed Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
title_sort Learning and Near-Optimal Control of Underactuated Surface Vessels With Periodic Disturbances
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Yinyan Zhang
Shuai Li
Jian Weng
format Journal article
container_title IEEE Transactions on Cybernetics
container_volume 52
container_issue 8
container_start_page 7453
publishDate 2022
institution Swansea University
issn 2168-2267
2168-2275
doi_str_mv 10.1109/tcyb.2020.3041368
publisher Institute of Electrical and Electronics Engineers (IEEE)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description In this article, we propose a novel learning and near-optimal control approach for underactuated surface (USV) vessels with unknown mismatched periodic external disturbances and unknown hydrodynamic parameters. Given a prior knowledge of the periods of the disturbances, an analytical near-optimal control law is derived through the approximation of the integral-type quadratic performance index with respect to the tracking error, where the equivalent unknown parameters are generated online by an auxiliary system that can learn the dynamics of the controlled system. It is proved that the state differences between the auxiliary system and the corresponding controlled USV vessel are globally asymptotically convergent to zero. Besides, the approach theoretically guarantees asymptotic optimality of the performance index. The efficacy of the method is demonstrated via simulations based on the real parameters of an USV vessel.
published_date 2022-08-01T14:02:59Z
_version_ 1821323852125306880
score 11.048042

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