Robust adaptive neural networks control for dynamic positioning of ships with unknown saturation and time-delay

In this paper, a robust adaptive neural networks control based on minimal?parameter-learning (MLP) is proposed for dynamic positioning (DP) of ships with unknown saturation, time delay, external disturbance and dynamic uncertainties. Through the velocities backstepping method, radial basis function (RBF) neural networks and robust adaptive control are incorporated to design a novel controller of which an appropriate LyapunovKrasovskii Function (LKF) is constructed to overcome the effect caused by time-delay. Meanwhile, the MLP technology is applied to reduce the computational burden while only one parameter need to be update by an adaptive law. In additional, a robust adaptive compensate term is introduced to estimate the bound of the lumped disturbance including the unknown saturation, unknown external disturbance and the approximate error of neural networks control while the robustness of MLP is improved and the unknown saturation is compensated. The developed control law makes the DP closed-loop system be uniformly ultimately stable which can be proved strictly through Lyapunov theory. Finally, simulations with a guidance law are proposed to demonstrate the validity of controller we developed.