This article considers the cooperative path-following control problem for a cluster of networked autonomous underwater vehicles (AUVs) suffering from unknown dynamics and ocean disturbances. By virtue of light-of-sight guidance and undirected graph, a synchronized guidance approach is created for underactuated AUVs, where multiple geometry curves are taken into account and information exchanges-related path variables are utilized, and thereby enabling AUVs to be synchronized and stabilized into a desired formation pattern. Within the distributed surge and yaw controller design, the unknown dynamics and the ocean disturbances are lumped together by using a linear state transformation. And a prediction-based fuzzy state observer (PFSO) is devised for estimating the unmeasured lumped states, where prediction errors are used to update fuzzy weights. Through the Lyapunov analysis, it is proven that surge and yaw-tracking errors and state observation errors are uniformly ultimately bounded. Simulation verifications are deployed to illustrate the efficacy and superiority of the designed method.

本文考虑了一群遭受未知动力学和海洋干扰的联网自动水下机器人（AUV）的协作路径跟随控制问题。借助视线引导和无向图​​，为欠驱动AUV创建了同步引导方法，其中考虑了多个几何曲线并利用了与信息交换相关的路径变量，从而使AUV得以同步和稳定形成所需的形成图案。在分布式浪涌和偏航控制器设计中，通过使用线性状态变换将未知动力学和海洋干扰集中在一起。设计了基于预测的模糊状态观测器（PFSO）来估计未测量的集总状态，其中使用预测误差来更新模糊权重。通过李雅普诺夫分析，可以证明浪涌和偏航跟踪误差以及状态观测误差最终是统一限定的。部署仿真验证来说明设计方法的有效性和优越性。