An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation least squares algorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation least squares algorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation least squares algorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation least squares algorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.

中文翻译：

---

基于多元创新最小二乘辨识算法的自主水下航行器参数辨识

准确的模型对于工程师为自主水下航行器设计稳健的控制器非常重要。在实践中，有两个因素使得识别难以获得 AUV 模型的准确参数。首先，自主水下航行器模型是一个耦合的六自由度模型，动力学模型的每个状态都会影响其他五个状态。其次，有100多个不同影响的水动力系数，有些参数太小无法识别。本文提出了一个包含基本参数的简化六自由度模型，并采用基于递归最小二乘算法的多创新最小二乘算法来获取参数。多创新最小二乘算法利用过去的几个误差来识别参数，识别结果比递归最小二乘算法更准确。它通过实验收集实际数据，并设计一个数值程序来识别模型参数。同时，将多创新最小二乘算法与递归最小二乘算法和最小二乘法的性能进行了比较，结果表明，多创新最小二乘算法是最有效的简化参数识别方法。六自由度模型。它通过实验收集实际数据，并设计一个数值程序来识别模型参数。同时，将多创新最小二乘算法与递归最小二乘算法和最小二乘法的性能进行了比较，结果表明，多创新最小二乘算法是最有效的简化参数识别方法。六自由度模型。它通过实验收集实际数据，并设计一个数值程序来识别模型参数。同时，将多创新最小二乘算法与递归最小二乘算法和最小二乘法的性能进行了比较，结果表明，多创新最小二乘算法是最有效的简化参数识别方法。六自由度模型。