Event-Driven Prescribed Optimal Disturbance Rejection for Dynamic Positioning of Ships via Reinforcement Learning

Dynamic positioning (DP) stands as the keystone underpinning ships’ operations in the abyssal depths and remote oceans. This paper proposes an event-driven disturbance rejection approximate optimal dynamic positioning scheme for surface ships with prescribed performance via reinforcement learning (RL). Firstly, a disturbance observer is established to achieve the online estimations of marine environmental disturbances such that the undesirable disturbance effects on control performance can be reduced. Meanwhile, the positioning error transformations with the prescribed performance function is established to combine with the backstepping method and the virtual controller can be designed to constrain the…

• NN
  National Natural Science Foundation of China

  Awards: 62533006, 62273172, 52471376, 52301418
• NS
  Natural Science Foundation of Shandong Province

  Award: ZR2024MF055
• YE
  Young Elite Scientists Sponsorship Program by Tianjin

  Award: tsqn202507228
• FR
  Fundamental Research Funds for the Central Universities

  Award: ZYGX2024Z018