綦志刚, 张昊, 李冰, 鲁喆. 基于改进型野马智能算法的船舶参数辨识方法[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03818
引用本文: 綦志刚, 张昊, 李冰, 鲁喆. 基于改进型野马智能算法的船舶参数辨识方法[J]. 中国舰船研究. DOI: 10.19693/j.issn.1673-3185.03818
A Ship Parameter Identification Method Based on Improved Wild Horse Optimizer[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03818
Citation: A Ship Parameter Identification Method Based on Improved Wild Horse Optimizer[J]. Chinese Journal of Ship Research. DOI: 10.19693/j.issn.1673-3185.03818

基于改进型野马智能算法的船舶参数辨识方法

A Ship Parameter Identification Method Based on Improved Wild Horse Optimizer

  • 摘要: 【目的】传统的参数辨识方法难以完成多维度、多自由度的综合参数辨识,为了实时获取船舶的复杂参数和姿态信息,保证船舶在航行时的稳定性和安全性,将改良型野马智能算法引入船舶参数辨识方法,与传统的船舶辨识方法相结合,提高船舶参数辨识精度。【方法】在建立船舶纵向运动模型的基础上,引入了动态惯性权重设计对改良型野马智能算法进一步优化,完成船舶纵向参数辨识方法的设计。【结果】通过对比并分析采用不同算法时船舶辨识模型的跟踪效果,以及在不同海浪遭遇角下的船舶参数辨识结果,发现了改良型野马智能算法的辨识误差为1%左右,远低于其他算法,从而使得该算法的辨识模型对航行时的船舶姿态具有更加精确的跟踪效果。【结论】该辨识方法可实时为船舶航行时提供精确的参数,提高了船舶航行时的可操作性,从而保证船舶航行时的稳定性和安全性。

     

    Abstract: Objectives Traditional parameter identification methods are difficult to achieve comprehensive parameter identification in multiple dimensions and degrees of freedom. In order to obtain real-time complex parameters and attitude information of ships, ensure the stability and safety of ships during navigation, an Improved Wild Horse Optimizer is introduced into the ship parameter identification method, which is combined with traditional ship identification methods to improve the accuracy of ship parameter identification. Methods On the basis of establishing a longitudinal motion model of the ship, a dynamic inertia weight design was introduced to further optimize the Improved Wild Horse Optimizer and complete the design of the ship longitudinal parameter identification method. Results By comparing and analyzing the tracking performance of ship identification models using different algorithms, as well as the identification results of ship parameters under different wave encounter angles, it was found that the improved Wild Horse optimizer has an identification error of about 1%, which is lower than other algorithms. Therefore, the identification model of this algorithm has a more accurate tracking effect on the ship's attitude during navigation. Conclusions This identification method can provide accurate parameters in real-time for ship navigation, improve the operability of ship navigation, and thus ensure the stability and safety of ship navigation.

     

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