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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

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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56084
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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 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.
College: College of Engineering
Issue: 8
Start Page: 7453
End Page: 7463