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

作　　者：ZHANG Qianqian KANG Yu YU Peilong ZHU Jin LIU Chunhan LI Pengfei 

机构地区：[1]Department of Automation,University of Science and Technology of China,Hefei 230026,China [2]Institute of Advanced Technology,University of Science and Technology of China,Hefei 230026,China

出　　处：《Journal of Systems Science & Complexity》2021年第3期843-859,共17页系统科学与复杂性学报（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant Nos.2018AAA0100800 and 2018YFE0106800;the National Natural Science Foundation of China under Grant Nos.61725304 and 61673361;the Science and Technology Major Project of Anhui Province under Grant No.912198698036。

摘　　要：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.

关 键 词：Controller design Euler-Maruyama approximate discretization Markov switching system mode clustering 

分 类 号：TP13[自动化与计算机技术—控制理论与控制工程]