ObjectiveThe traditional model predictive control method employs a repeated online optimization approach, resulting in a high computational burden for underactuated ship path-following predictive controller. To address this issue, this paper presents an efficient predictive controller for underactuated ship path following based on the neurodynamic optimization system. Method First, the line-of-sight (LOS) guidance principle is employed to mitigate the underactuated problem herein; for kinematic model uncertainty in traditional LOS guidance law, a robust LOS guidance method based on the sliding mode concept is proposed. Furthermore, the sideslip angle induced by external disturbances negatively affects path following. To compensate for this effect, a robust adaptive LOS guidance method is proposed, enhancing robustness against model uncertainty and external disturbances. Second, in order to address the input saturation problem, the model predictive control is adopted herein to transform ship path following problem into the quadratic optimization problem with input constraints. Finally, the neurodynamic optimization solver is proposed based on the projection recurrent neural network herein to solve the quadratic optimization problem with input constraints, enhancing the computational efficiency.ResultsIn this study, both simulations for straight line path following and curved line path following are conducted. Overall, the simulation results show that the presented efficient predictive controller can achieve arbitrary path following. Additionally, the comparative simulations are performed, revealing that the presented method exhibits advantage in computational efficiency compared to the Fmincon optimization solver. Specifically, the neurodynamic optimization solver achieves approximately a 90-fold improvement in computational efficiency compared to the Fmincon optimization solver. ConclusionThe research results have practical value for improving the real-time performance of underactuated ship path following. In the future, the proposed real-time predictive control method will be extended to the application of multi-ship cooperative predictive control.