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Cited by 263

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Robust Adaptive Position Mooring Control for Marine Vessels

Chen, Mou, Ge, Shuzhi Sam, How, Bernard Voon Ee, Choo, Yoo Sang·2013·IEEE Transactions on Control Systems Technology

Abstract

In this paper, robust adaptive control with dynamic control allocation is proposed for the positioning of marine vessels equipped with a thruster assisted mooring system, in the presence of parametric uncertainties, unknown disturbances and input nonlinearities. Using neural network approximation and variable structure based techniques in combination with backstepping and Lyapunov synthesis, the positioning control is developed to handle the uncertainties, input saturation and dead-zone characteristics of the mooring lines and thrusters. Full state feedback with all states measurable and output feedback using high gain observer to estimate unmeasurable states are considered. Dynamic control allocation is presented for actuation of the position mooring system. Under the proposed robust adaptive control, semi-global uniform boundedness of the closed-loop signals are guaranteed. Numerical simulations are carried out to show the effectiveness of the proposed control.

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Crossref · 10.1109/tcst.2012.2183676

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Metadata

Authors
Chen, M., Ge, S. S., How, B. V. E., & Choo, Y. S.
Venue
IEEE Transactions on Control Systems Technology
Published
2013-03
Volume
21
Issue
2
Pages
395-409
ISSN
  • 1063-6536 (ISSN-L)
  • 1558-0865
OpenAlex
W2094437443
Cited by
263
References
14
FWCI
21.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Robust Adaptive Position Mooring Control for Marine Vessels. https://app.cochat.ai/papers/10.1109/tcst.2012.2183676
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Chen, M., Ge, S. S., How, B. V. E., & Choo, Y. S. (2013). Robust Adaptive Position Mooring Control for Marine Vessels. IEEE Transactions on Control Systems Technology. https://doi.org/10.1109/tcst.2012.2183676

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.