A Novel Parameter Identification Algorithm for 3-DOF Ship Maneuvering Modelling Using Nonlinear Multi-Innovation

In order to further explore more efficient identification algorithms that can solve the ship motion identification modeling problem, a novel identification algorithm for 3-DOF ship maneuvering modeling is proposed in this paper. Based on the recursive least-squares method, the proposed algorithm com...

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Bibliographic Details
Published inJournal of marine science and engineering Vol. 10; no. 5; p. 581
Main Authors Zhao, Baigang, Zhang, Xianku, Liang, Cailei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2022
Subjects
3-DOF
Accuracy
Algorithms
Artificial intelligence
Cargo ships
Container ships
Control theory
Convergence
Identification
Innovation
Innovations
Kalman filters
Least squares method
Mathematical models
Modelling
Neural networks
nonlinear multi-innovation
Parameter identification
Ship handling
ship maneuvering model
Ship motion
Ships
Simulation
Simulators
Support vector machines
Velocity
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Summary:In order to further explore more efficient identification algorithms that can solve the ship motion identification modeling problem, a novel identification algorithm for 3-DOF ship maneuvering modeling is proposed in this paper. Based on the recursive least-squares method, the proposed algorithm combines multi-innovation and nonlinear innovation techniques that focus on the innovation’s processing. In addition, its convergence capability is theoretically demonstrated. The identification algorithm shows good accuracy and convergence that is verified by a simulation experiment on a container ship. Furthermore, another simulation experiment using the full mission ship handling simulator demonstrates its generalization capability.
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse10050581