Neural Network Control for a Class of Stochastic Nonlinear Switched System Based on Backstepping

摘要

In this paper, we deal with the switched stabilization problem for a class of stochastic switched nonlinear systems based on RBF neural network and backstepping approach. By using the combination design technique of backstepping and neural network, an adaptive neural network switching controller is designed for the switched stabilization of stochastic switched nonlinear system with trigonal structure. It is shown that, under Stochastic Lasalle Theorem, the resulting closed-loop system is proved to be globally asymptotically stable in probability.