Adaptive Nonlinear Model Predictive Control for Autonomous Surface Vessels With Largely Varying Payload

Abstract

Autonomous surface vessels (ASVs) always carry payloads such as passengers and cargoes. The change in the payload can sometimes be several times the weight of the vessel. The payload can cause significant changes in the dynamics of the vessel, thereby degrading the performance of the controller. This paper proposes an adaptive nonlinear model predictive control (A-NMPC) strategy for ASV trajectory tracking, which allows real-time changes in dynamics caused by severe payload variation. First, a nonlinear dynamic model that updates with the vessel’s payload is established. Then a pressure sensing method is proposed to estimate the payload of the vessel. Further, a parametric cost function that considers changing dynamics, as well as input and state constraints, is formulated in the NMPC algorithm. The tracking ability of A-NMPC is systematically studied on three different sizes of vessels in the simulation where the payload of these vessels changes eight times their inherent weight. Numerical results show that when the payload changes greatly the vessels with A-NMPC can accurately track the reference trajectory while the vessels with conventional NMPC cannot. Finally, the tracking experiments with a quarter-scale vessel in a swimming pool further verify the effectiveness of the proposed A-NMPC strategy.