Nonlinear System Identification for Heading and Pitch Control of a Tethered Uncrewed Underwater Vehicle in Changing and Uncertain Environments

In this paper, authors proposed a motion control system for a fully actuated uncrewed underwater vehicle (UUV) considering heading and pitch dynamics. The concerned dynamics of the vehicle is identified by using a polynomial representation of Nonlinear Auto-Regressive Moving Average with eXogenous input (NARMAX) model with the help of an onboard Doppler velocity log (DVL) sensor. An Orthogonal Forward Regression (OFR) algorithm is used to determine the structure of the identified model. The parameters of the model are selected based on the Error Reduction Ratio (ERR) criterion and are updated using a Recursive Extended Least Squares (RELS) algorithm. To achieve autonomous heading reference creation a Nonlinear Model Predictive Controller (NMPC) is designed. The NMPC uses the measurement model based on position measurement from an onboard Short Base Line (SBL) acoustical positioning system to generate a reference velocity input. A NARMAX model-based adaptive gain controller is designed to track the reference velocities. The proposed control scheme is validated both numerically and experimentally showing that dynamics of vehicle can be captured successfully by the proposed method. It is also shown that the proposed controller can effectively track the desired references.