Adaptive neural networks trajectory tracking control for autonomous underwater helicopters with prescribed performance

Abstract

An autonomous underwater helicopter (AUH) is a type of autonomous underwater vehicle (AUV) that is capable of fixed-point hovering, precise and free take-off, and landing. It is effective for underwater ultra-mobile tasks, including mobile observation networks, resource exploration, and data connection. This paper aims to investigate an AUH trajectory tracking controller based on the prescribed performance method, considering the influence factors such as current disturbance, modeling uncertainty and thruster faults. A novel preset time performance function is designed in which the stable terminal time of the system can be specified explicitly, and the convergence rate of the system's dynamic process may be altered by adjusting the parameters, making it more intuitive for the designer. A speed observer was introduced to address this problem of measuring the required state information from the AUH's sensors in practice, which was enhanced using a radial basis function neural network (RBFNN) to approximate external perturbations and uncertainties. The stability of the closed-loop AUH system is proved by using Lyapunov theory. The feasibility and effectiveness of the proposed algorithm proposed approach were eventually verified by two sets of simulations for different thruster fault forms.