ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs
ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs
About this item
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Author / Creator
Zhao, Chunbo , Yan, Huaran and Gao, Deyi
Publisher
United States: PeerJ. Ltd
Journal title
Language
English
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Publisher
United States: PeerJ. Ltd
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Scope and Contents
Contents
In this work, we propose a robust self-learning control scheme based on action-dependent heuristic dynamic programming (ADHDP) to tackle the 3D trajectory tracking control problem of underactuated uncrewed underwater vehicles (UUVs) with uncertain dynamics and time-varying ocean disturbances. Initially, the radial basis function neural network is i...
Alternative Titles
Full title
ADHDP-based robust self-learning 3D trajectory tracking control for underactuated UUVs
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_c608b069dc1c4f0aaf497cf9e9e3b1a8
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_c608b069dc1c4f0aaf497cf9e9e3b1a8
Other Identifiers
ISSN
2376-5992
E-ISSN
2376-5992
DOI
10.7717/peerj-cs.2605
