Global approximation based adaptive RBF neural network control for supercavitating vehicles

Abstract

A global approximation based adaptive radial basis function (RBF) neural network control strategy is proposed for the trajectory tracking control of supercavitating vehicles (SV). A nominal model is built firstly with the unknown disturbance. Next, the control scheme is established consisting of a computed torque controller (CTC) for the practical vehicle and an RBF neural network controller to estimate model error between the practical vehicle and the nominal model. The network weights are adapted by employing a Lyapunov-based design. Then it is shown by the Lyapunov theory that the trajectory tracking errors asymptotically converge to a small neighborhood of zero. The control performance of the proposed controller is illustrated by simulation.