Preliminary Evaluation of Null-Space Dynamic Process Model Identification with Application to Cooperative Navigation of Underwater Vehicles

This paper reports a method and preliminary evaluation of a novel null-space least-squares parameter identification method for a fully nonlinear second-order 6-degree-of-freedom (DOF) dynamic process model of an underactuated underwater vehicle (UV) for which both the model parameters and the control-input parameters are unknown. This paper further reports the application of the identified plant models in combined underwater communication and navigation (cooperative navigation) of UVs. We report an approach to model identification that simultaneously identifies 6-DOF UV nonlinear plant-model parameters, control-surface parameters, and thruster-model parameters. We believe this approach is suitable for identifying plant model parameters from data obtained in full-scale experimental trials of UVs in controlled motion. The reported approach to nonlinear model identification of UVs is evaluated in simulation studies. The resulting identified UV plant models are further evaluated in simulated cooperative navigation missions of the UV that are representative of high-precision survey missions. To the best of our knowledge, this paper reports the first method to identify 6-DOF UV model parameters, control-surface parameters, and thruster-model parameters simultaneously.