Adaptive Learning of Inland Ship Power Propulsion under Environmental Disturbances

Nicolas Dann, Pablo Segovia, Vasso Reppa

Research output: Contribution to journalConference articleScientificpeer-review

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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.

Original languageEnglish
Pages (from-to)1-6
Issue number31
Publication statusPublished - 2022
Event14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, CAMS 2022 - Kongens Lyngby, Germany
Duration: 14 Sep 202216 Sep 2022


  • On-line learning scheme
  • shallow water
  • ship resistance
  • speed-power prediction
  • surface vehicles


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