Learning‐based hierarchical control for fault‐tolerant cooperation of networked marine surface vehicles

In this article, an efficient hierarchical control framework is proposed to address the cooperation problems (e.g., consensus tracking, formation tracking, and time‐varying formation tracking) for the networked marine surface vehicles in the presence of external disturbances, actuator faults and failures. Based on this framework, several learning‐based hierarchical control algorithms are developed, involving an iterative learning‐based estimator and a local observer‐based finite‐time controller. The estimator is designed to achieve sufficiently precise estimation of the leader states through enough iterations, while the observer‐based finite‐time controller is used to observe and compensate the dynamic uncertainties as well as stabilize the error states in a finite time. By using the theories of Hurwitz, Schur, and Lyapunov stability, the sufficient conditions for guaranteeing the convergence of these learning‐based hierarchical control algorithms are derived. Finally, numerical simulations are performed on the Cyber‐Ships II to verify the effectiveness of the presented algorithms.