Ship Manoeuvering Modelling with a Physics-Oriented Neural Network-Based Approach

This paper proposes a novel system identification schema to obtain a model of a ship manouvering process using artificial Neural Networks (ANNs), computational models that have changed the research paradigm, bringing remarkable advantages in several fields. The ANNs capabilities in modelling nonlinear dynamical systems are undeniable and several approaches have been proposed in recent years. In our work, an ordinary black-box approach used to determine the input-output relationship of the system under investigation is initially outlined. Then, a physics-oriented approach to train a recurrent neural network structure is provided and thoroughly explained, investigating also the possible adoption of a simpler network structure. The results obtained with the physics-oriented approach in modelling the ship manouvering process are significantly better than the ones achieved with the pure black-box approach. Consequently, the physics-oriented approach resulted to be an exceptional tool for inferring the physical laws behind a nonlinear system accounting for a limited amount of data. The effectiveness of this method motivates further studies to evaluate its possible implementation in model-based control algorithms.