Robust adaptive NN control of dynamically positioned vessels under input constraints

Abstract

Abstract A robust adaptive neural network (NN) control law is developed for dynamic positioning (DP) of vessels with unknown dynamics and unknown time-varying disturbances under input constraints through incorporating adaptive radial basis function (RBF) NNs, an auxiliary dynamic system and a robust control term into dynamic surface control method. The developed DP control law makes the DP closed-loop system be uniformly ultimately stable and the vessel's position and heading be maintained at the desired values with arbitrarily small errors. The advantages of the proposed control scheme are that: first, the developed DP control law does not require any priori knowledge of vessel dynamics and disturbances under input constraints, and prevents the presence of input constraints from degrading control performance and even destabilizing the DP control system; second, the developed DP control law compensates for not only unknown time-varying disturbances but also NN approximation errors for unknown vessel dynamics. Simulations on two supply vessels are conducted to exhibit the efficiency and control performance of the developed DP control law.