The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm. (27th April 2020)

• Record Type: Journal Article
• Authors: Huajun, Zhang; Xinchi, Tong; Hang, Guo; Shou, Xia
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 2(2020:Mar./Apr.)
• Publication Date: 2020-04-27
• Subjects: Autonomous underwater vehicle -- multi-innovation least squares algorithm -- parameter identification -- recursive least squares algorithm -- hydrodynamic coefficients; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420921016
• Languages: English
• ISSNs: 1729-8806

Abstract

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.