Abstract: Objectives To solve the problems of communication resource limitation and parameter uncertainty in the dynamic positioning control task of fully driven ships in Marine engineering practice Methods this paper presents a robust event-triggered control algorithm for ship dynamic positioning considering the dynamic characteristics of actuators. The algorithm uses RBF neural network to approximate the system uncertainty. At the same time, a novel event triggering mechanism in sensor-controller channel is designed by introducing a zero-order hold, which reduces the signal transmission frequency in sensor-controller and controller-actuator channels and greatly saves the communication resources of the system. In addition, the adaptive parameters updated online are designed to compensate for the gain uncertainty of the actuator. These adaptive parameters are updated aperiodically based on the designed event trigger mechanism, which reduces the computational load of the algorithm and ensures that the ship can perform the dynamic positioning task stably. Results the Lyapunov stability theory is used to prove that all error variables in the closed-loop control system satisfy semi-global uniformly final bounded (SGUUB) stability, and the effectiveness of the proposed algorithm is verified by setting comparison simulation.