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Thruster-assisted position mooring (PM) systems use both mooring lines and thrusters for station keeping of marine structures in ocean environments. To operate in an energy-efficient manner in moderate sea conditions, setpoints need to be appropriately chosen for the setpoint controller, so that the mooring system counteracts main environmental loads, while the thrusters reduce oscillatory motions of the marine structure. In this paper, reinforcement learning is used to design a decision-making agent for setpoint selection. In particular, a deep deterministic policy gradient (DDPG) approach is adopted with the powerful actor–critic architecture to continuously modify the setpoint setting at an optimal position. Extensive numerical experiments demonstrated that with the DDPG-based PM system, the intelligent agent is able to successfully identify the optimal positioning region in an unknown and stochastic environment, and the power consumption of the thrusters is maintained at a considerably low level.

For dynamic positioning systems, a three degree-of-freedom motion control in the horizontal plane has usually been regarded as adequate for practical applications. However, for marine structures with a small water-plane area and low metacentric height, unintentional surge–pitch coupled motion will be induced by thruster actions. To effectively mitigate the thruster-induced pitch motion, we first apply a pitch damping controller for the dynamic positioning of a semisubmersible platform. Quantitative studies are conducted to select the optimal control coefficient for this damping controller, and its influence on surge–pitch coupled motion is analyzed theoretically and numerically. Furthermore, a novel adaptive fuzzy damping controller is proposed to improve the pitch mitigating effect. The fuzzy controller takes low-frequency pitch angle and pitch rate as inputs, and outputs time-varying damping control coefficient through fuzzy inference. Comparisons are made between the fixed damping controller and the proposed fuzzy damping controller. Finally, a parametric analysis is conducted to investigate the influence of the maximum damping control coefficient on pitch motion. The overall simulation results show that the proposed fuzzy damping controller has better performance than the fixed damping controller.