Robust Adaptive Dynamic Surface Control for Synchronized Path Following of Multiple Underactuated Autonomous Underwater Vehicles

In this paper, we consider the problem of synchronized path following for multiple underactuated autonomous underwater vehicles subject to parametric modeling uncertainty and unmodeled dynamics. Nonlinear path following controllers are proposed for individual vehicles that yield convergence of the position tracking errors to a small neighborhood of the origin. Vehicles coordination is reached through synchronizing the along-path speed and path variables, by using a mixture of tools from graph theory and Lyapunov theory. The developed neural network (NN) adaptive technique allows to handle the parametric modeling uncertainty and unmodeled dynamics, without requiring the accurate knowledge of the model. The proposed dynamic surface control (DSC) technique simplifies the synchronized path following controllers by introducing the first-order filters. A complete stability analysis is provided to illustrate that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results validate the performance and robustness improvement of the proposed strategy.