Wu, Junzhuo2025-07-142025-07-142025-07-14https://hdl.handle.net/10222/85208This thesis investigates the development and implementation of model predictive control (MPC) frameworks for autonomous sailboat control under different wind conditions. The research is motivated by the need for robust, constraint-aware control strategies for sailboats, that can effectively handle the nonlinear dynamics and environmental uncertainties. The study begins with a comprehensive modelling of sailboat dynamics, incorporating sailboat kinematic and kinetics and wave disturbances. Two simulation-based studies both using MPC are then conducted. The first study focuses on sail angle optimization and trajectory tracking, where the control objective is to maximize sailing speed with safety considerations and then achieve accurate trajectory tracking. The second study addresses stochastic wind sailing, where an NMPC-based path planning and tracking controller is designed and evaluated. This controller integrates the controllability analysis to improve performance under both deterministic and stochastic wind conditions. Comparative simulations highlight the advantages of the proposed approach. Then, an experimental platform is instrumented on a small-scale physical autonomous sailboat. This platform integrates the ArduPilot control stack, hardware components and supporting software infrastructure. System identification techniques are applied to extract sailboat’s dynamic model, and experimental trials are conducted to assess the controller performance in real-world conditions. The results demonstrate that MPC offers a viable and effective solution for autonomous sailboat control, capable of addressing both operational constraints and environmental variability.enModel Predictive ControlOptimization