基于正弦函数处理新息的船舶模型参数辨识新算法

New identification algorithm for ship model parameters based on sinusoidal function processing innovation

  • 摘要:
    目的 当船舶试验数据样本少时,将面临难以快速、准确辨识船舶模型参数的难题。
    方法 基于最小二乘系统辨识算法,采用正弦函数处理新息,提出一种基于正弦函数非线性新息处理的船舶模型参数辨识算法。以“育鲲”船为例进行仿真实验,在只有26组辨识数据的情况下,对比最小二乘法和非线性新息改进最小二乘法的模型参数辨识效果,并利用“育鹏”船对所提算法的有效性进行验证。
    结果 结果表明:与最小二乘法相比,非线性新息改进最小二乘法的模型参数辨识精度提升了15%左右。
    结论 研究结果可为小样本数据情况下的船舶模型参数辨识提供参考。

     

    Abstract:
    Objective When few ship test data samples are available, it is difficult to identify ship model parameters quickly and accurately.
    Methods On the basis of the original least-square system identification algorithm, an innovative sinusoidal function process is introduced, and a new ship model parameter identification method based on the nonlinear sinusoidal function is proposed.The ship Yukun, a teaching and training ship of Dalian Maritime University, is selected for the identification experiment. With only 26 test data samples, the identification effects of the original least square method and improved least square algorithm are compared.
    Results The simulation results show that the parameter identification accuracy of the algorithm is improved by about 15%, and the effectiveness of the algorithm is verified using the ship Yupeng.
    Conclusion This algorithm provides valuable references for the parameter identification of ship models with few test data samples.

     

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