Automated Sailboat Navigation using Controllabilty-Aware Nonlinear Model Predicitive Control under Stochastic Winds

Abstract

This paper presents a systematic approach to plan and execute a time-efficient sailboat trajectory under the challenging stochastic wind conditions for automated sailboat. Unlike deterministic scenarios, stochastic wind disturbances introduce challenges such as gust unpredictability, directional shifts, and fluctuating apparent wind speeds. These characteristics render traditional planning methods, such as Line-of-Sight (LoS) and waypoint approaches, to be ineffective and not applicable, as they often rely on static or deterministic environmental models. This paper proposed a new path planner and controller based on nonlinear model predictive control method. It is compared with a baseline planner and controller. A Lie-algebraic analysis of the sailboat dynamics is carried out to identify the operating conditions under which the vessel loses first-order control authority in surge, and these conditions are embedded as constraints in the NMPC. Finally, simulation results are provided to validate the proposed framework.