Abstract
Todays model-based dynamic positioning (DP) systems require that the ship and thruster dynamics are known with some accuracy in order to use 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 persistently excited under DP. In addition the ship parameter estimation problem is nonlinear and multivariable with only position and thruster state measurements available for parameter estimation. The process and measurement noise most also be modelled in order to avoid parameter drift due to environmental disturbances and sensor failure. This article discusses an offline parallel extended Kalman filter (EKF) algorithm utilizing two measurement series in parallel to estimate the parameters in the DP ship model. Full-scale experiments with a supply vessel are used to demonstrate the convergence and robustness of the proposed parameter estimator.
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