Experimental Identification of Six-Degree-of-Freedom Coupled Dynamic Plant Models for Underwater Robot Vehicles

This paper addresses the modeling and experimental identification of six different six degree-of-freedom (6-DOF) coupled nonlinear second-order plant models. We report a comparative experimental evaluation of six different candidate plant models whose unknown plant parameters are estimated from data obtained in free-motion vehicle trials. The parameter estimation methodologies of ordinary least squares (OLS), total least squares (TLS), and underdetermined TLS were employed to identify experimentally the unknown plant model parameters. We evaluate the performance of each of the six different 6-DOF coupled nonlinear finite-dimensional plant models of an underwater vehicle estimated by OLS and the plant model identified by TLS by comparing the mean absolute error between the experimentally observed vehicle velocities and the velocities obtained by a numerical simulation of the identified plant models. We also report a cross validation which evaluates the performance of a plant model to accurately reproduce observed plant velocities for experimental trials differing from the trials from which the plant model parameters were estimated. We conclude that: 1) plant models identified by TLS generally perform better (i.e., more accurately reproduce observed experimental behavior) than models identified by OLS; and 2) plant models including fully parametrized coupled quadratic drag terms perform best overall in cross validation. This study has the following contributions: it is the first reported experimental 6-DOF fully coupled plant model identification and cross validation of a low-speed, fully actuated, and neutrally buoyant underwater vehicles; it is the first experimental 6-DOF plant model identification for this same class of underwater vehicles during free-flight experiments; and it is the first reported use of TLS to perform 6-DOF plant model identification of an underwater vehicle.