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基于实船试验数据的船舶四自由度辅助模型非线性创新最小二乘辨识算法

Auxiliary model nonlinear innovation least squares algorithm for identification ship 4-DOF via full-scale test data.

作者信息

Song Chunyu, Li Yinfu, Sui Jianghua

机构信息

Navigation and Ship Engineering College, Dalian Ocean University, Dalian, 116023, China.

出版信息

Sci Rep. 2024 Oct 28;14(1):25861. doi: 10.1038/s41598-024-73081-9.

DOI:10.1038/s41598-024-73081-9
PMID:39468098
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11519898/
Abstract

For the ship motion with large inertia coupled system identification modeling inaccuracy, the ship scale effect and the existence of partial unmeasured ship data problems. In this paper, an auxiliary model nonlinear innovation least squares identification algorithm is proposed. The new algorithm uses the output of the auxiliary model instead of the unmeasurable variables in the full-scale test data of the ship, and optimizes the error using the tangent function. Compared with the existing algorithm, the error of the improved algorithm decreases with the increase of time and continuously approaches to zero, which greatly improves the identification accuracy and convergence efficiency. The results show that the improved algorithm has significant identification accuracy and reliability. The identification method designed in this paper can be applied to the field of ship intelligent navigation engineering.

摘要

针对船舶运动中存在大惯性耦合系统辨识建模不准确、船舶尺度效应以及部分船舶数据不可测等问题。本文提出了一种辅助模型非线性创新最小二乘辨识算法。该新算法利用辅助模型的输出代替船舶实船测试数据中不可测变量，并采用正切函数对误差进行优化。与现有算法相比，改进算法的误差随时间增加而减小并不断趋近于零，大大提高了辨识精度和收敛效率。结果表明，改进算法具有显著的辨识精度和可靠性。本文设计的辨识方法可应用于船舶智能导航工程领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/86b245d442d2/41598_2024_73081_Fig10_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/9200a67def21/41598_2024_73081_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/d47b6b2a99cb/41598_2024_73081_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/86b245d442d2/41598_2024_73081_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/caced85d4289/41598_2024_73081_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/94930764e6ab/41598_2024_73081_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/6c43c8d47f53/41598_2024_73081_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/925471de0472/41598_2024_73081_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/720768fb731a/41598_2024_73081_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/881cce8f6171/41598_2024_73081_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/3b99afbc04b3/41598_2024_73081_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/9200a67def21/41598_2024_73081_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/d47b6b2a99cb/41598_2024_73081_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7645/11519898/86b245d442d2/41598_2024_73081_Fig10_HTML.jpg

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本文引用的文献

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IEEE Trans Cybern. 2022 Dec;52(12):12916-12928. doi: 10.1109/TCYB.2021.3091580. Epub 2022 Nov 18.
2
Indoor Smartphone Localization Based on LOS and NLOS Identification.基于 LOS 和 NLOS 识别的室内智能手机定位。
Sensors (Basel). 2018 Nov 16;18(11):3987. doi: 10.3390/s18113987.
3
Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data.
基于全尺度试验数据的四自由度船舶操纵建模的多创新自构最小二乘辨识
ISA Trans. 2015 Sep;58:186-95. doi: 10.1016/j.isatra.2015.04.004. Epub 2015 May 2.
4
A novel DVS guidance principle and robust adaptive path-following control for underactuated ships using low frequency gain-learning.一种基于低频增益学习的欠驱动船舶新型 DVS 制导原理和鲁棒自适应路径跟踪控制。
ISA Trans. 2015 May;56:75-85. doi: 10.1016/j.isatra.2014.12.002. Epub 2015 Jan 8.