Robust adaptive neural network control for dynamic positioning of marine vessels with prescribed performance under model uncertainties and input saturation

Abstract

This paper presents a dedicated robust adaptive neural network control (RANNC) scheme for dynamic positioning (DP) of marine vessels with a prescribed performance under model uncertainties, external disturbances and input saturation. To guarantee that the transient performance is always within the prescribed performance constraints, a novel error transfer function is firstly proposed to transfer the original dynamic positioning system with the constrained error behavior into an equivalent unconstrained one. Next, the RANNC scheme is developed with the backstepping technique, and the method of command filter is introduced to avoid the ”dimension disaster” problem. Considering the ship’s inertial matrix and damping matrix are not constant in practice, the adaptive control technique and RBF neural networks are adopted to design the model adaptive controller, which only requires the actuator matrix rather than a completely accurate model. Moreover, minimal learning parameter technique is introduced to minimize the computational burden triggered by weight update of neural networks. Finally, comparison simulation results are provided to illustrate the performance of the proposed RANNC scheme.