Adaptive Learning and Optimization of High-Speed Sailing Maneuvers for America's Cup

Abstract

This paper presents an adaptive control strategy to optimize the sailing maneuvers of an AC75 foiling sailboat competing in America's Cup. Foiling yachts have nonlinear, high-dimensional, and unstable dynamics due to several articulations for fast motions and maneuverability. Achieving aggressive and optimal maneuvers requires taking these complex dynamics into account instead of analytical optimizations using reduced-order models. We compared extremum-seeking and Jacobian learning (JL) control approaches on a full-order model to achieve optimal maneuvers and used JL to optimize articulations. The controllers were integrated with a high-fidelity sailboat simulator for safe and efficient maneuver optimization. The optimal solutions were subject to physical/actuator constraints and those enforced to ensure the feasibility of the maneuvers by humans (sailors). The close-hauled and tacking maneuvers were optimized to achieve maximum velocity made good (VMG) and minimum loss of VMG, respectively. The optimal maneuvers boast a marginal VMG loss of less than 1.5%, which enables exploiting areas of good wind conditions in the racing environment.