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Real-Time Parameter Estimation of a Nonlinear Vessel Steering Model Using a Support Vector Machine

Xu, Haitong; Hinostroza, M. A.; Hassani, Vahid; Guedes Soares, C
Journal article, Peer reviewed
Accepted version
View/Open
Xu (Locked)
URI
http://hdl.handle.net/11250/2640461
Date
2019
Metadata
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Collections
  • Institutt for marin teknikk [3643]
  • Publikasjoner fra CRIStin - NTNU [41955]
Original version
10.1115/1.4043806
Abstract
The least-square support vector machine (LS-SVM) is used to estimate the dynamic parameters of a nonlinear marine vessel steering model in real-time. First, maneuvering tests are carried out based on a scaled free-running ship model. The parameters are estimated using standard LS-SVM and compared with the theoretical solutions. Then, an online version, a sequential least-square support vector machine, is derived and used to estimate the parameters of vessel steering in real-time. The results are compared with the values estimated by standard LS-SVM with batched training data. By comparison, a sequential least-square support vector machine can dynamically estimate the parameters successfully, and it can be used for designing a dynamic model-based controller of marine vessels.
Publisher
ASME
Journal
Journal of Offshore Mechanics and Arctic Engineering

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