发表机构
Dalhousie University; Carleton University（达尔豪斯大学; 卡尔顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种基于可控性感知非线性模型预测控制的帆船自动导航方法，通过李代数分析识别失控条件并嵌入约束，仿真验证其时间高效性。
AI 中文摘要
本文提出了一种系统方法，用于在具有挑战性的随机风条件下为自动帆船规划并执行时间高效的帆船轨迹。与确定性场景不同，随机风扰动带来了阵风不可预测性、风向变化和视风速波动等挑战。这些特性使得传统规划方法（如视线法（LoS）和航路点方法）失效且不适用，因为它们通常依赖于静态或确定性的环境模型。本文提出了一种基于非线性模型预测控制方法的新型路径规划器与控制器，并与基线规划器和控制器进行了比较。对帆船动力学进行了李代数分析，以识别船舶在纵荡方向上失去一阶控制权的运行条件，并将这些条件作为约束嵌入到NMPC中。最后，提供了仿真结果以验证所提出的框架。
英文摘要
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.
