Event Triggered Control of Ship Automatic Berthing Based on Network Attack

This article proposes an adaptive neural network event triggered control scheme based on virtual parameter learning for ship automatic berthing control under false data injection (FDI) attack environment, which is simultaneously affected by external interference, uncertain dynamics, and input saturation. In the controller design process, neural network technology and adaptive virtual parameter learning methods are combined to perform online reconstruction compensation for actuator signal mutations, uncertain dynamics, and external disturbances caused by FDI attacks. In addition, event triggering mechanism is adopted to reduce communication load and channel occupancy rate. Finally, the stability of the control law was analyzed using Lyapunov theory, and the effectiveness of the scheme was verified through comparative simulation experiments. The simulation results showed that the control algorithm proposed in this paper has superior control performance and steady-state performance in FDI attack environments. In practical engineering applications, this scheme can provide theoretical support for the automatic berthing of intelligent ships.