Adaptive neural control for a class of stochastic nonlinear systems using stochastic small-gain theorem

摘要

In this paper, a novel adaptive neural control scheme is presented for a class of stochastic strict-feedback nonlinear systems with dead-zone model and unmodeled dynamics using stochastic small-gain theorem. Radial basis function neural networks (RBFNNs) are utilized to approximate the unknown continuous functions. Compared with the existing work, the controller is simpler and the restriction of dynamic disturbances is relaxed. The stability analysis is given to show that all the signals in the closed-loop system are ISpS in probability. The effectiveness of the proposed design is illustrated by simulation results.