Adaptive NN Control for a Class of Stochastic Nonlinear Systems with Unmodeled Dynamics

Abstract

Abstract This paper addresses the problem of adaptive neural networks output feedback control for a class of stochastic nonlinear system with unmodeled dynamics. Only a neural network (NN) is employed to compensate for all unknown nonlinear functions, so that the designed controller is simpler than the existing results and reduces the computation loads. With the concept of input-to-state practical stability (ISpS) and nonlinear small-gain theorem extended to the stochastic case, together with the RBF NN technique, an adaptive NN output feedback controller is proposed. It is shown that the solutions of the closed-loop system are bounded in probability.