详细信息
Distributed H-infinity-consensus filtering for piecewise discrete-time linear systems ( SCI-EXPANDED收录)
文献类型:期刊文献
英文题名:Distributed H-infinity-consensus filtering for piecewise discrete-time linear systems
作者:Han, Fei[1,2];Wei, Guoliang[3];Song, Yan[3];Li, Wangyan[1]
第一作者:韩非;Han, Fei
通讯作者:Wei, GL[1]
机构:[1]Univ Shanghai Sci & Technol, Sch Business, Shanghai 200093, Peoples R China;[2]Xinxiang Univ, Dept Math & Informat Sci, Xinxiang 453003, Peoples R China;[3]Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai Key Lab Modern Opt Syst, Shanghai 200093, Peoples R China
第一机构:Univ Shanghai Sci & Technol, Sch Business, Shanghai 200093, Peoples R China
通讯机构:[1]corresponding author), Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai Key Lab Modern Opt Syst, Shanghai 200093, Peoples R China.
年份:2015
卷号:352
期号:5
起止页码:2029-2046
外文期刊名:JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
收录:;WOS:【SCI-EXPANDED(收录号:WOS:000353871600013)】;
基金:This work was supported in part by the Program for New Century Excellent Talents in University under Grant NCET-11-1051, the National Natural Science Foundation of China under Grants 61374039, 61203143, the Innovation Fund Project For Graduate Student of Shanghai JWCXSL1401.
语种:英文
摘要:This paper is concerned with the distributed H-infinity-consensus filtering problem for a class of piecewise discretetime linear systems. Firstly, the modes and their transitions of augmented piecewise linear systems as well as distributed filters are formulated. Also, the structure of augmented distributed filter gains is presented in virtue of the adjacent matrix of sensor networks. Then, a set of sufficient conditions are provided for the distributed filter to ensure that its dynamics is global asymptotically stable with the H-infinity-consensus performance constraint. In addition, the distributed filter gains are obtained with the aid of the convex optimal method. At last, an illustrative simulation is presented to demonstrate the effectiveness and applicability of the proposed distributed filtering algorithm. (C) 2015 The Franklin Institute. Published by Elsevief Ltd. All rights reserved.
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