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:
    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,将改良型野马优化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 建立船舶纵向运动模型,引入动态惯性权重设计对改良型野马优化算法进一步优化,完成船舶纵向参数辨识方法设计。
    结果 通过对比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,发现改良型野马优化算法的辨识误差为1%左右,远低于其他算法,表明基于该算法的辨识模型能更精确地跟踪航行时的船舶姿态。
    结论 该辨识方法可实时为船舶航行提供精确的参数,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:
    Objective It is difficult to achieve comprehensive parameter identification in multiple dimensions 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 is introduced into the ship parameter identification method. It is then combined with traditional ship identification methods to improve the accuracy of ship parameter 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 Improved Wild Horse Optimizer and complete the design of the 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 is found that the Improved Wild Horse Optimizer 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 stability and safety of ship navigation.

     

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