<p>This paper presents a novel regulation-aware cooperative path following (CPF) control method for maritime autonomous surface ships (MASSs). The proposed method considers both the constraints of the Convention on International Regulations for Preventing Collisions at Sea and fully unknown model parameters. At the kinematic level, firstly, a distance-triggered decision mechanism is introduced to assess the encounter status between MASSs and traffic ships. Then, a regulation-aware heading guidance law is designed by integrating a line-of-sight approach and a constant angle avoidance algorithm, ensuring compliance with COLREGs during avoidance maneuvering. Next, a safe CPF kinematic controller is designed to achieve the vessel train formation based on the path variable containment approach. At the kinetic level, to address the unknown and varying model parameters as well as unknown external disturbance, a predictor-based adaptive extended state observer is proposed, enabling the estimation of unknown model parameters and lumped disturbances. Subsequently, an anti-disturbance kinetic controller is designed to precisely track the kinematic control signals. The input-to-state stability of the closed-loop system is analyzed through Lyapunov theory. Finally, the effectiveness of the proposed method is validated by simulation results.</p>

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Regulation-aware cooperative path following control of maritime autonomous surface ships with fully unknown model parameters

  • Hao Feng,
  • Lu Liu,
  • Bing Han,
  • Tieshan Li,
  • Yuchao Wang,
  • Zhouhua Peng

摘要

This paper presents a novel regulation-aware cooperative path following (CPF) control method for maritime autonomous surface ships (MASSs). The proposed method considers both the constraints of the Convention on International Regulations for Preventing Collisions at Sea and fully unknown model parameters. At the kinematic level, firstly, a distance-triggered decision mechanism is introduced to assess the encounter status between MASSs and traffic ships. Then, a regulation-aware heading guidance law is designed by integrating a line-of-sight approach and a constant angle avoidance algorithm, ensuring compliance with COLREGs during avoidance maneuvering. Next, a safe CPF kinematic controller is designed to achieve the vessel train formation based on the path variable containment approach. At the kinetic level, to address the unknown and varying model parameters as well as unknown external disturbance, a predictor-based adaptive extended state observer is proposed, enabling the estimation of unknown model parameters and lumped disturbances. Subsequently, an anti-disturbance kinetic controller is designed to precisely track the kinematic control signals. The input-to-state stability of the closed-loop system is analyzed through Lyapunov theory. Finally, the effectiveness of the proposed method is validated by simulation results.