Direct Adaptive Control of Underwater Vehicles using Neural Networks

Abstract

A direct adaptive neural network controller is developed for a model of an underwater vehicle. A radial basis neural network and a multilayer neural network are used in the closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the plant are required, and this scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the boundedness of signals are guaranteed. A comparison of the results with different neural network architecture is made, and the performance of the controller is demonstrated by computer simulations.