Renato presented at the ALCOS 2022, Adaptive & Learning Control Systems Conference, IFAC.
Title: Adaptive Learning for Maximum Takeoff Efficiency of High-Speed Sailboats
Abstract: This paper presents an optimal takeoff maneuver for an AC75 foiling sailboat competing in the America’s Cup. The innovative sailboat design introduces extra degrees of freedom and articulations in the boat that result in nonlinear, high-dimensional, and unstable dynamics. The optimal maneuvers were achieved by exploring out-of-the-box solutions through adaptive control and optimization. We used a high-fidelity sailboat simulator for the data generation process and an adaptive control approach (Jacobian Learning (JL)) to optimize the sailing maneuver. Takeoff is a dynamic sailboat maneuver that involves transitioning the boat from a low-speed in-water status (displacement mode) to a high-speed out-of-water status (foiling mode) via actuation of the sailboat’s inputs. We optimized the time for the boat’s transitions from displacement mode to foiling mode while maximizing the projection of the velocity (Velocity Made Good (VMG)) in the desired target direction (True Wind Angle (TWA)). Furthermore, we optimized the sailboat’s upwind steady-state performance (closed-haul VMG) for varying sailing directions (TWA) and used the optimal TWA to formulate the takeoff.
The optimal solution is subject to physical/actuator constraints and the ones enforced to ensure the feasibility of the maneuvers by humans (sailors). The optimal takeoff achieved an average VMG of 7.42~m/s. This maneuver serves as a performance benchmark for the sailors and provides insightful information about the underlying dynamics of the boat.
IFAC-PapersOnLine: Volume 55, Issue 12, 2022, Pages 402-407