Event-triggered finite-time fault-tolerant position-mooring control of offshore vessels subject to state constraints and actuator failures

In this article, the event-triggered finite-time fault-tolerant adaptive neural network control strategy is developed for the position mooring systems subject to state constraints and actuator failures. To tackle practical issues within underactuated vessels mooring systems including position and velocity constraints, thruster faults, and inherent nonlinear hydrodynamic damping characteristics, an intelligent control algorithm is constructed by integrating logarithm-type barrier Lyapunov functions with the backstepping method. Radial basis function neural networks (RBFNNs) are utilized to approximate the unknown nonlinear hydrodynamic damping dynamics embedded in the mathematical model of vessels mooring systems. The event-triggered mechanism is incorporated to alleviate redundant data transmission overhead and conserve limited communication bandwidth of the closed-loop vessels mooring system, and the adaptive update laws are formulated to counteract thruster fault impacts. The Lyapunov stability analysis proves that all closed-loop system signals are uniformly ultimately bounded, and the tracking error converges to a small compact neighborhood of the origin within finite time. Comparative numerical simulations validate that the proposed control scheme achieves favorable performance in fault tolerance and state constraint compliance for offshore vessels.