Adaptive Learning of Inland Ship Power Propulsion under Environmental Disturbances

Journal Article (2022)
Authors

Nicolas Dann (Student TU Delft)

P. Segovia Castillo (TU Delft - Transport Engineering and Logistics)

V. Reppa (TU Delft - Transport Engineering and Logistics)

Research Group
Transport Engineering and Logistics
Copyright
© 2022 Nicolas Dann, P. Segovia Castillo, V. Reppa
To reference this document use:
https://doi.org/10.1016/j.ifacol.2022.10.400
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Nicolas Dann, P. Segovia Castillo, V. Reppa
Research Group
Transport Engineering and Logistics
Issue number
31
Volume number
55
Pages (from-to)
1-6
DOI:
https://doi.org/10.1016/j.ifacol.2022.10.400
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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.