This is the Way: Mitigating the Roll of an Autonomous Uncrewed Surface Vessel in Wavy Conditions Using Model Predictive Control
Daniel L. Jenkins, Joshua A. Marshall
TL;DR
This work addresses roll mitigation for under-actuated USVs operating in wavy conditions by developing a nonlinear model predictive control (NMPC) framework based on a full $6$-DOF Fossen-style dynamics model. A tunable roll-cost term within the NMPC drives open-water waypoint navigation while reducing roll, demonstrated in simulations of a Maritime Robotics Otter USV under a single sinusoidal wave model with horizon $P$ and sampling $T$, solved via CasADi/IPOPT. Results show a $39\%$ decrease in average roll in 1.75 m sinusoidal waves, with intuitive behaviors such as tacking emerging from weight tuning; a practical, real-time capable controller is thus feasible. The paper outlines a clear path to real-time wave-prediction integration and real-world field trials, which could substantially enhance safety and performance of small USVs in realistic sea states.
Abstract
Though larger vessels may be well-equipped to deal with wavy conditions, smaller vessels are often more susceptible to disturbances. This paper explores the development of a nonlinear model predictive control (NMPC) system for Uncrewed Surface Vessels (USVs) in wavy conditions to minimize average roll. The NMPC is based on a prediction method that uses information about the vessel's dynamics and an assumed wave model. This method is able to mitigate the roll of an under-actuated USV in a variety of conditions by adjusting the weights of the cost function. The results show a reduction of 39% of average roll with a tuned controller in conditions with 1.75-metre sinusoidal waves. A general and intuitive tuning strategy is established. This preliminary work is a proof of concept which sets the stage for the leveraging of wave prediction methodologies to perform planning and control in real time for USVs in real-world scenarios and field trials.
