Comparison of Advanced Modeling Approaches for Autonomous Docking of Fully Actuated Vessels. Issue 31 (2022)

• Record Type: Journal Article
• Authors: Homburger, H.; Wirtensohn, S.; Diehl, M.; Reuter, J.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 31(2022)
• Page Start: 451
• Page End: 456
• Publication Date: 2022
• Subjects: Nonlinear system identification -- Statistical data analysis -- Maritime systems; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.10.469
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper presents a systematic comparison of different advanced approaches for motion prediction of vessels for docking scenarios. Therefore, a conventional nonlinear gray-box-model, its extension to a hybrid model using an additional regression neural network (RNN) and a black-box-model only based on an RNN are compared. The optimal hyperparameters are found by grid search. The training and validation data for the different models is collected in full-scale experiments using the solar research vessel Solgenia. The performances of the different prediction models are compared in full-scale scenarios. These can improve advanced control strategies e.g., nonlinear model predictive control (NMPC) or reinforcement learning (RL). This paper explores the question of what the advantages and disadvantages of the different presented prediction approaches are and how they can be used to improve the docking behavior of a vessel. Videos are available under:https://www.htwg-konstanz.de/en/research-and-transfer/institutes-and-laboratories/isd/control-engineering/videos/