文献类型：期刊文献

英文题名：Local condition-based finite-horizon distributed H∞-consensus filtering for random parameter system with event-triggering protocols

作者：Han, Fei[1,2]; Song, Yan[3]; Zhang, Sunjie[3]; Li, Wangyan[1]

第一作者：Han, Fei;韩非

通讯作者：Song, Yan

机构：[1] Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China; [2] Department of Mathematics and Information Science, Xinxiang University, Xinxiang, 453003, China; [3] Shanghai Key Lab of Modern Optical System, Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

第一机构：Business School, University of Shanghai for Science and Technology, Shanghai, 200093, China

年份：2017

卷号：219

起止页码：221-231

外文期刊名：Neurocomputing

收录：EI(收录号：20164603017350);Scopus(收录号：2-s2.0-84994408774)

基金：This work was supported in part by the Program for Capability Construction of Shanghai Provincial Universities under Grant 15550502500, the National Natural Science Foundation of China under Grants 61403254 and 61374039, the China Postdoctoral Science Foundation Grant 2016M590369.

语种：英文

外文关键词：Nonlinear control systems - Sensor networks - Stochastic systems

摘要：This paper investigates the distributed H∞-consensus filtering problem for a class of discrete time-varying systems with random parameters and event-triggering protocols. An event-triggering protocol for each node is employed to reduce the burden of the network communication. A novel matrix named by information matrix is proposed to describe the complicated correlations among the elements of random matrix. By virtue of the presented information matrix, a weighted covariance matrix can be easily obtained to analyze the system with random parameters. With the aid of the newly constructed dissipation matrix and vector supplied rate functions, a set of local coupled conditions for each node is obtained such that the stochastic vector dissipativity-like over the finite-horizon of the filtering error dynamics can be guaranteed. As well, these sufficient conditions together could effectively solve the distributed H∞-consensus filtering problem. Notably, the designed filtering algorithm can be implemented on each node to obtain the desirable distributed filter gains. Finally, the effectiveness and applicability of the proposed algorithm is illustrated by a numerically simulative example. ? 2016 Elsevier B.V.