Adaptive Learning of Inland Ship Power Propulsion under Environmental Disturbances
Nicolas Dann (Student TU Delft)
Pablo Segovia (TU Delft - Transport Engineering and Logistics)
Vasso Reppa (TU Delft - Transport Engineering and Logistics)
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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.
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