Robust model predictive control for path-following of underactuated surface vessels with roll constraints

Abrupt maneuvering or strong disturbances from sea waves probably lead to large roll motion of underactuated surface vessels, which severely affects stability of surface vessels. A novel path-following control for vessels with roll constraints is proposed by combining Kalman filter, disturbance observer and robust constrained model predictive control method. Firstly, a switched linear uncertain model with coupled surge, sway, roll and yaw is constructed for underactuated surface vessels. Then, an improved Sage-Husa Adaptive Kalman Filter is proposed to estimate vessels' full states by partial states information and eliminate the effects of measurement noises. Next, the disturbance observer, which is used to estimate low-frequency disturbances from wind, waves and oceans, is combined with constrained model predictive control to improve the robustness of the closed-loop control system. In this paper, the effectiveness of the proposed algorithm is verified by theoretical analysis, simulations and experiments.