A discretization approach to sampled‐data stabilization of networked systems with successive packet losses

This article is concerned with the stabilization problem for a class of networked systems subject to successive packet losses. Different from the input delay approach used in some existing literature, the continuous‐time system under consideration is first converted into a discrete‐time stochastic s...

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Published inInternational journal of robust and nonlinear control Vol. 31; no. 10; pp. 4589 - 4601
Main Authors Hu, Zhipei, Zhang, Jin, Deng, Feiqi, Fan, Zhun, Qiu, Li
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 10.07.2021
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ISSN1049-8923
1099-1239
DOI10.1002/rnc.5490

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Summary:This article is concerned with the stabilization problem for a class of networked systems subject to successive packet losses. Different from the input delay approach used in some existing literature, the continuous‐time system under consideration is first converted into a discrete‐time stochastic system with system matrices subject to stochastic characteristic. In order to deal with the difficulties in the calculations of mathematical expectations of both matrix exponential and integral of matrix exponential function, the upper bound of successive packet losses and packet drop rate are assumed to be known and then the probabilities of the number of successive packet losses taking each value in a bounded set are calculated. Based on this, stability criteria are derived by recurring to the law of total expectation, which guarantee the exponential mean‐square stability of resulting closed‐loop discrete‐time stochastic system with a prescribed H∞ performance. Moreover, a controller design procedure is then proposed. Finally, to verify the analysis results and testify the effectiveness and applicability of the designed algorithm, a numerical simulation example is given.
Bibliography:Funding information
National Natural Science Foundation of China, 61733008; 61873099; 61873170; 62003204; 62073144; U1813225; Shantou University Scientific Research Foundation for Talents, NTF19031; Natural Science Foundation of Guangdong Province, 2020A1515010441; Science and Technology Development Foundation of the Shenzhen Government, JCYJ20190808144607400; Guangzhou Science and Technology Planning Project, 202002030158; 202002030389
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.5490