Autonomous Ship Utility Model Parameter Estimation Utilising Extended Kalman Filter

Abstract

In this paper, a problem of autonomous ship utility model identification for control purposes is considered. In particular, the problem is formulated in terms of model parameter estimation (one-step-ahead prediction). This is a complex task due to lack of measurements of the parameter values, their time-variability and structural uncertainty introduced by the available models. In this work, authors consider and compare two utility models based on often utilised ship model structures with time-varying parameters identified recursively using the extended Kalman lter (EKF). The validation results have been obtained using simulation experiments in which the required information for the parameter estimation task had been generated using a cognitive model of B-481 ship. The results indicate the benefits and drawbacks, in terms of estimation accuracy and computational complexity, of using each of the investigated utility model structures.