Identification of an Autonomous Underwater Vehicle Dynamic Using Extended Kalman Filter with ARMA Noise Model

In the procedure of designing an underwater vehicle or robot, its maneuverability and controllability must be simulated and tested, before the product is finalized for manufacturing. Since the hydrodynamic forces and moments highly affect the dynamic and maneuverability of the system, they must be estimated with a reasonable accuracy. In this study, hydrodynamic coefficients of an autonomous underwater vehicle (AUV) are identified using velocity and displacement measurements, and implementing an Extended Kalman Filter (EKF) estimator. The hydrodynamic coefficients are included in the augmented state vector of a six DOF nonlinear model. The accuracy and the speed of the convergence of the algorithm are improved by selecting a proper covariance matrix using the ARMA process model. This algorithm is used to estimate the hydrodynamic coefficients of two different sample AUVs: NPS AUV II and ISIMI. The comparison of the outputs of the identified models and the outputs of the real simulated models confirms the accuracy of the identification algorithm. This identification method can be used as an efficient tool for evaluating the hydrodynamic coefficients of underwater vehicles (robots), using the experimental data obtained from the test runs.