To solve the problems of communication resource limitations and parameter uncertainty in the dynamic positioning control tasks of fully driven ships in marine engineering applications, this paper presents a robust event-triggered control algorithm for ship dynamic positioning that considers the dynamic characteristics of the actuators.

The algorithm uses a radial basis function (RBF) neural network to approximate system uncertainty. At the same time, a novel event-triggering mechanism in the sensor-controller channel is designed by introducing a zero-order hold which reduces the signal transmission frequency in the sensor−controller and controller−actuator channels, thus greatly saving the communication resources of the system. In addition, the adaptive parameters are updated online and designed to compensate for the gain uncertainty of the actuators, which reduces the computational load and ensures that the ship can perform dynamic positioning tasks stably.

The Lyapunov stability theory is used to prove that all error variables in the closed-loop control system satisfy semi-global uniformly ultimately bounded (SGUUB) stability, and the effectiveness of the proposed algorithm is verified through comparison with a simulation.

The results of this study can provide useful references for promoting the development of intelligent ship equipment.