In this paper, a novel disturbance observer based control algorithm is proposed for marine surface vessels in the presence of environmental disturbance, model uncertainty and servo-system uncertainty. An adaptive finite-time disturbance observer (AFTDO) is embedded into the proposed control strategy that facilitates the robustness against the environmental disturbance. By fusion of the neural networks (NNs) and minimal learning parameterization (MLP) techniques, the control algorithm is developed without the priori information about ship model and the number of the adaptive parameters are reduced. Furthermore, the semi global finite-time uniformly boundedness (SGFTUB) of the closed-loop system is proved by the Lyapunov theory. By employing a supply vessel as the plant, numerical simulations are conducted to demonstrate the effectiveness and superiority of the proposed algorithm.


In recent years, as the extension of people's activity in ocean environment, more advanced marine equipments are required for ships in practical engineering. Dynamic positioning system (DPS) is a kind of automatic control system whose function is to maintain the desired position and attitude for marine vehicles in the presence of environmental disturbance. Due to the excellent flexibility and maneuverability, DPS has been extensively mounted in marine vehicles, such as supply vessel, floating production off-loading and storage units (FPSOs), offshore oil rigs, cable and pipe layers.

Due to the practical significance of DPS in ocean engineering, various of researches (Sorensen, 2011, Pettersen et al., 2000, Peter et al., 1998) about the DPS have been investigated in the past years. Using only position measurements, a PD controller is presented for dynamic positioning ships in the presence of external disturbance in (Loria et al., 2000). Focus on the compensation of time-varying disturbance, a weather optimal position control (WOPC) strategy is developed for marine vessels in (Fossen et al., 2001). For the output feedback cases, disturbance observers are constructed to facilitate the robustness of control scheme. In (Do et al., 2011), an observer based controller is designed to achieve the dynamic positioning control of marine surface ships under environmental disturbance, and the globally asymptotically stability of the closed-loop system is guaranteed by the Lyapunov theory. Benefit from the universal approximation capability of NNs, both the position and velocity are estimated precisely for DPS under noisy corruption in (Lin et al., 2014). In (Du et al., 2015), an output feedback control algorithm is proposed for fully actuated ships by fusion of dynamic surface control (DSC) and vectorial Backstepping techniques. In (Chen et al., 2009), both the system uncertainties and input saturation are tackled by robust adaptive control scheme for marine surface vessels. In order to achieve a faster convergence, a terminal sliding mode control algorithm is proposed for Unmanned Surface vessels in (Lv et al., 2016, Wang et al., 2016). A robust finitetime H control algorithm is designed for dynamic positioning vessels with input delay in (Lin et al., 2018). More than that, literatures about thrust allocation have also reported in (Sordalen, 1997, Ambrosinao et al., 1989).

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