Dynamic surface control for non-strict-feedback stochastic nonlinear interconnected systems

Abstract

In this paper, a neural-network-based dynamic surface control method is developed for a class of non-strict-feedback stochastic nonlinear interconnected systems. Neural-networks (NNS) combined with adaptive backstepping technique are applied to model the unknown nonlinear functions of the stochastic interconnected system. The dynamic surface control (DSC) method is adopted to ensure the computation burden is greatly reduced. The proposed controllers guarantee the closed-loop interconnected stochastic nonlinear system is globally bounded stable in probability.