Adaptive neural network finite-time optimal control for unmanned surface vehicle system

Abstract

This article investigates the adaptive neural network (NN) optimal control design problem for unmanned surface vehicle (USV) systems by finite-time control theory. A new adaptive finite-time NN optimal control policy is developed, which is composed of a NN adaptive feed-forward controller and an optimal error feedback controller. The former is constructed by using backstepping recursive control design algorithm and the latter is designed by using adaptive dynamic programming (ADP) theory. It is demonstrated that developed finite-time optimal control strategy is able to ensure the USV system is stable in a finite-time interval and achieve optimal control performance. Moreover, it can handle the computational complexity problem existing in previous finite-time optimal control methods. Comparison and simulation results illustrate the validity and superiority of the developed optimal control concept.