Adaptive identification of dynamically positioned underwater robotic vehicles

This paper reports a stable online adaptive identification technique for the identification of finite-dimensional dynamical models of dynamically positioned underwater robotic vehicles. Proofs for the identifier’s global stability, and for the input-tostate stability of this class of plants are reported. A direct comparison of the adaptive identification method to a conventional, offline, least-squares method is reported. Using experimental data obtained with the Johns Hopkins University Remotely Operated underwater robotic Vehicle (JHUROV), both methods are employed to identify decoupled, single-degree-of-freedom dynamical plant models. Performance of the resulting identified dynamical plant models is quantitatively compared to the experimentally observed motion of the actual vehicle.