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An improved nonlinear innovation-based parameter identification algorithm for ship models

Published online by Cambridge University Press:  05 March 2021

Baigang Zhao Open the ORCID record for Baigang Zhao [Opens in a new window]  and
Xianku Zhang
Baigang Zhao
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
Xianku Zhang*
Affiliation:
Navigation College, Dalian Maritime University, Dalian, China
*
*Corresponding author. E-mail: zhangxk@dlmu.edu.cn
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Abstract

To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Keywords

ship modelparameter identificationnonlinear innovationstochastic gradient
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 3 , May 2021 , pp. 549 - 557
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Copyright
Copyright © The Royal Institute of Navigation 2021

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