Data-Driven Adaptive Control of Unmanned Surface Vehicles Using Learning-Based Model Predictive Control
In this thesis, the subject of data-driven control of Unmanned Surface Vehicles (USVs) is explored. The control task is formulated through Nonlinear Model Predictive Path Following Control (NMPFC). System identification (SYSID) and Reinforcement Learning (RL) are employed to improve performance in a data-driven manner. The objectives were to assess the resulting controller’s path-following ability