Cascade-based tracking control for dynamic positioning vessels under unknown sea loads

This paper investigates the trajectory tracking control problem of a dynamic positioning vessel (DPV) subject to tracking performance, time-varying sea loads and inertia uncertainties. Firstly, a novel cascade model of the DPV is built to describe the dynamics. Then, in order to guarantee the performance constraints of the trajectory tracking, a new-type fixed-time performance function is proposed, which can not only guarantee the transient and steady-state performance during task execution, but also achieve convergence at a fixed time. In addition, an adaptive neural network is introduced to handle the problem of parameter uncertainty and unknown sea loads in the system. Furthermore, it is proved by the Lyapunov stability theory that all signals in the closed-loop system are bounded . Finally, numerical simulation results verified the effectiveness of the proposed method and ensure that the tracking error is stable in the interval of 2 m or less within a given time.