IDENTIFICATION OF DYNAMICALLY POSITIONED SHIPS

Today's model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to us e linear quadratic optimal control theory. However, it is difficult to identify the mathematical model of a dynamically positioned (DP) ship , since the ship is not persist entry excited under DP. In add it ion, the ship parameter-estimation problem is nonlinear and multivariable, with only position and thruster state measurements available for para meter estimation. The process and measurement noise must also be model ed in order to avoid parameter drift due to environmental disturbances and sensor failure. This article discusses an off-line parallel exten ded Kalman filter (EKF) algorithm utilizing two measurement series in parallel to estimate the parameters in the DP ship model. Full-scale e xperiments with a supply vessel are used to demonstrate the convergenc e and robustness of the proposed parameter estimator.