Event-Sampled Adaptive Neural Automatic Berthing Control for Underactuated Ships Under FDI Attacks

This work addresses the automatic berthing control problem of underactuated ships under false data injection (FDI) attack, and an event-sampled automatic berthing control scheme is proposed. To avoid the FDI attack signals from entering the closed-loop system through the sensor–controller channel and worsening the berthing control performance as much as possible, a novel event-sampled adaptive neural network state observer is developed, which is independent of the controller. To solve the control design problem of berthing caused by underactuated features, an equivalent motion model of underactuated ships under FDI attack is established by differential homeomorphic transformation. Furthermore, under the backstepping design framework, using the state observer and adaptive neural network technology, a single-parameter learning-based automatic berthing control solution is developed. Meanwhile, to further reduce the network resource consumption and load caused by the transmission of control signals, an event-triggered mechanism for the controller–actuator channel is established. The theoretical analysis by Lyapunov indicates that the constructed closed-loop system for automatic berthing control is stable, and all the signals are bounded. Simulation and comparison are carried out to verify the effectiveness and superiority of proposed control scheme, and the results verify the conclusions and theoretical feasibility of this work.