Lepeng Chen
Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results
Chen, Lepeng; Cui, Rongxin; Yang, Chenguang; Yan, Weisheng
Abstract
In this paper, an adaptive trajectory tracking
control algorithm for underactuated unmanned surface
vessels (USVs) with guaranteed transient performance is
proposed. To meet the realistic dynamical model of USVs,
we consider that the mass and damping matrices are not
diagonal and the input saturation problem. Neural Networks
(NNs) are employed to approximate the unknown external
disturbances and uncertain hydrodynamics of USVs. Moreover,
both full state feedback control and output feedback
control are presented, and the unmeasurable velocities of
the output feedback controller are estimated via a highgain
observer. Unlike the conventional control methods,
we employ the error transformation function to guarantee
the transient tracking performance. Both simulation and
experimental results are carried out to validate the superior
performance via comparing with traditional potential integral
(PI) control approaches.
Citation
Chen, L., Cui, R., Yang, C., & Yan, W. (2020). Adaptive neural network control of underactuated surface vessels with guaranteed transient performance: Theory and experimental results. IEEE Transactions on Industrial Electronics, 67(5), 4024-4035. https://doi.org/10.1109/TIE.2019.2914631
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 8, 2019 |
Online Publication Date | May 14, 2019 |
Publication Date | May 1, 2020 |
Deposit Date | May 16, 2019 |
Publicly Available Date | Mar 28, 2024 |
Journal | IEEE Transactions on Industrial Electronics |
Print ISSN | 0278-0046 |
Electronic ISSN | 1557-9948 |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 67 |
Issue | 5 |
Pages | 4024-4035 |
DOI | https://doi.org/10.1109/TIE.2019.2914631 |
Keywords | neural network, underactuated surface vessel, guaranteed transient performance |
Public URL | https://uwe-repository.worktribe.com/output/846933 |
Publisher URL | http://doi.org/10.1109/TIE.2019.2914631 |
Additional Information | Additional Information : (c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. |
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