The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs.

中文翻译：

---

基于最大似然法的多螺旋桨自动驾驶水下航行器建模

多螺旋桨自动驾驶水下航行器（AUV）的流体动力学特性通常很复杂，很难获得准确的数学模型。提出了基于CFD计算和最大似然识别算法的建模方法。首先，通过CFD计算获得了AUV船体的大致水动力参数。其次，在粗糙参数的基础上，提出了一种最大似然识别算法来调整参数，提高模型精度。此外，结合AUV模型结构特点，分析了提高识别算法收敛性的方法。最后，通过实验数据验证了识别算法和识别结果。发现该方法具有良好的收敛性和适应性。特别是，在不同的识别实验中，旋转力和转矩参数的识别结果高度一致，表明该方法能够很好地提取AUV的操纵特性，从而为AUV的控制器设计做出了贡献。本文的研究在多螺旋桨水下机器人的建模与控制中具有潜在的应用前景。