Sampled-Data Stabilization of a Class of Stochastic Nonlinear Markov Switching System with Indistinguishable Modes Based on the Approximate Discrete-Time Models |
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Authors: | Zhang Qianqian Kang Yu Yu Peilong Zhu Jin Liu Chunhan Li Pengfei |
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Institution: | 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 ; |
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Abstract: | 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. |
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