Sampled-data stabilization for a class of stochastic nonlinear systems based on the approximate discrete-time models

In this paper, we mainly focus on the sampled-data exponential stabilization in mean square for a class of stochastic nonlinear systems based on their Euler-Maruyama model. The relation of the pth moment exponential stability between the sampled-data systems and its exact discrete-time models is discussed; then we present Lyapunov theorem for pth moment exponential stability of discrete-time stochastic nonlinear systems and its converse theorem; the conditions for exponential stabilization in mean square for such sampled-data stochastic nonlinear systems based on their Euler-Maruyama approximation are given. The results provide a simpler and more convenient controller design method for such sampled-data systems, and the effectiveness of the approach is demonstrated in simulation example.