Adaptive Learning of Inland Ship Power Propulsion under Environmental Disturbances

Journal article (2022)

Authors

Nicolas Dann Student
P. Segovia Castillo Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
V. Reppa Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering
Research Group
Transport Engineering and Logistics (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2022 Nicolas Dann, P. Segovia Castillo, V. Reppa
DOI: https://doi.org/10.1016/j.ifacol.2022.10.400
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Published Date

2022

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Marine and Transport Technology

Research Group

Transport Engineering and Logistics

Abstract

This paper presents an adaptive approximation-based scheme for learning a partially known ship power propulsion plant under various environmental conditions. Considering the effect of water depth on the engine power, a dynamic model is defined comprised of the engine dynamics and the 1-DoF ship manoeuvring dynamics. The modelling challenge is the determination of ship resistance. To meet this challenge analytical modelling of ship resistance is combined with an error-filtering online learning (EFOL) scheme for computing an approximation of the unmodeled part of ship resistance related to wind and air. After simulations under multiple weather conditions, the trained model was demonstrated to efficiently estimate the unmodelled part of the ship resistance for an inland vessel.