Sampled-Data Stabilization of a Class of Stochastic Nonlinear Markov Switching System with Indistinguishable Modes Based on the Approximate Discrete-Time Models

This paper investigates the stabilization issue for a class of sampled-data nonlinear Markov switching system with indistinguishable modes.In order to handle indistinguishable modes,the authors reconstruct the original mode space by mode clustering method,forming a new merged Markov switching system.By specifying the difference between the Euler-Maruyama(EM)approximate discrete-time model of the merged system and the exact discrete-time model of the original Markov switching system,the authors prove that the sampled-data controller,designed for the merged system based on its EM approximation,can exponentially stabilize the original system in mean square sense.Finally,a numerical example is given to illustrate the effectiveness of the method.