4.7 Article

Synchronization Control of Dynamic Positioning Ships Using Model Predictive Control

Journal

Publisher

MDPI
DOI: 10.3390/jmse9111239

Keywords

dynamic positioning (DP); synchronization; model predictive control (MPC); neurodynamic optimization

Funding

  1. National Natural Science Foundation of China [52101397, U1813203, U1801262, 51879024, 51779029]
  2. Funds for Dalian Distinguished Young Scholar [2020RQ014]
  3. National Postdoctoral Program for Innovative Talent [BX201700041]
  4. Fundamental Research Funds of Central Universities [3132021131, 3132021139]

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This paper presents a novel synchronization controller based on model predictive control (MPC) for dynamic positioning (DP) ships to achieve underway replenishment. The controller ensures synchronization of position, orientation, and velocities, handles control input constraints, improves computational efficiency, and stability by incorporating a terminal cost function from the Lyapunov equation. Extensive simulations demonstrate the effectiveness and advantages of the proposed control design.
Underway replenishment is essential for ships performing long-term missions at sea, which can be formulated into the problem of leader-tracking configuration. Not only the position and orientation but also the velocities are required to be controlled for ensuring the synchronization; additionally, the control inputs are constrained. On this basis, in this paper, a novel synchronization controller on account of model predictive control (MPC) for dynamic positioning (DP) ships is devised to achieve underway replenishment. Firstly, a novel synchronization controller based on MPC is devised to ensure the synchronization of not only the position and orientation but the velocities; furthermore, it is a beneficial solution for its advantages in handling the control input constraints ignored in most studies of underway replenishment. Secondly, a neurodynamic optimization system is applied to the implementation of MPC, which can improve the computational efficiency and shorten the simulation time. Thirdly, stability, frequently neglected by traditional MPC, is ensured by the means of adding a terminal cost function exported from the Lyapunov equation into the objective function. Finally, the effectiveness and advantages of the proposed control design are illustrated by extensive simulations.

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