Autonomous Docking: System identification, Model Predictive Control and control allocation of marine vessels

Sammanfattning

This thesis investigates autonomous docking for monohull marine vessels, with theobjective of developing a generalized maneuvering framework applicable across multiplemonohull vessels. In today’s application regarding autonomous docking, thesolutions are often highly vessel-specific, which limits the ability to adapt.To address this limitation, this thesis proposes a general maneuvering script capableof identifying different vessel dynamics and supporting the vessel in critical low-speeddocking scenarios while maintaining safe and accurate behavior.The proposed approach integrates vessel dynamic modeling together with advancedcontrol and optimization techniques, including genetic algorithm, Model PredictiveControl, and control allocation methods. A mathematical vessel model is usedto approximate real-world behavior, which limits the controller and optimizer torealistic results. The genetic optimization process is used to tune the dynamicmodel based on real vessel behavior using recorded data, while Model PredictiveControl ensures optimal decision-making over a finite horizon. The control allocatoris applied to distribute control inputs among the available actuators in a feasibleand efficient manner.The results show that the proposed framework performs well in simulation environmentsand on a simulation rig, successfully executing autonomous docking maneuverswhile adapting to various vessel characteristics. However, performance inreal-world open-water experiments was limited due to model inaccuracies, hardwareconstraints, and time limitations, indicating that further development and testingare required for reliable real-world application.