Fast random sampling and small noise analysis for stochastic control models

In this paper, we study a linear control system with a given state feedback law. The system is influenced by rapid random sampling occurring at frequency $\frac{1}{n},n\in \mathbb{N}$, as well as by white noise of small intensity $\epsilon \in (0,1]$. We study the behavior of the system as $n\to \mathrm{\infty }$ and $\epsilon \searrow 0$ jointly, and prove that it converges to its ideal deterministic analogue. For the random fluctuations around its analogous deterministic trajectory, we obtain either stochastic differential equations or an ordinary differential equation depending on the joint behavior of $\epsilon$ and $n$. Further, we extend this problem to a nonlinear system driven by multiplicative white noise, where the noise intensity is scaled by a small parameter. In this case, we again perform a similar analysis as in the linear case.