中国舰船研究2025，Vol.20Issue(1)：85-95,11.DOI:10.19693/j.issn.1673-3185.03818
基于改进型野马优化算法的船舶参数辨识方法
Ship parameter identification method based on improved wild horse optimizer
摘要
Abstract
[Objective]It is difficult to achieve comprehensive parameter identification in multiple dimen-sions and degrees of freedom using traditional parameter identification methods.In order to obtain the real-time complex parameters and attitude information of ships,and ensure the stability and safety of ships during navigation,an improved wild horse optimizer(IWHO)is introduced into the ship parameter identification method.It is then combined with traditional ship identification methods to improve the accuracy of ship para-meter identification.[Method]On the basis of establishing a longitudinal motion model of the ship,a dynamic inertia weight design is introduced to further optimize the wild horse optimizer and complete the design of the longitudinal parameter identification method.[Results]By comparing and analyzing the track-ing performance of ship identification models using different algorithms,as well as the identification results of ship parameters under different wave encounter angles,it is found that IWHO has an identification error of about 1%,which is lower than those of other algorithms.Therefore,the identification model of this algorithm has a more accurate tracking effect on the ship's attitude during navigation.[Conclusion]The proposed identification method can provide accurate parameters in real time,improve operability and ensure the stabil-ity and safety of ship navigation.关键词
船舶运动建模/辨识方法设计/野马优化算法/动态惯性权重/参数辨识Key words
ship motion modeling/identification method design/improved wild horse optimizer(IWHO)/dynamic inertia weight/identification(control systems)分类
交通工程引用本文复制引用
綦志刚,张昊,李冰,鲁喆..基于改进型野马优化算法的船舶参数辨识方法[J].中国舰船研究,2025,20(1):85-95,11.基金项目
黑龙江省自然科学基金资助项目(LH2019E035) （LH2019E035）
黑龙江省教育教学改革重点项目(SJGZ20210013) （SJGZ20210013）
黑龙江省教育教学改革项目(SJGY20220120) （SJGY20220120）
黑龙江省教育科学规划重点课题(GJB1320064) （GJB1320064）
哈尔滨工程大学教育改革项目(2023B0405) （2023B0405）
