Local condition-based finite-horizon distributed H_∞-consensus filtering for random parameter system with event-triggering protocols

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• 作者：Fei Han^a ; ^bAuthor Vitae ; Yan Song^c ; ^{sonya_usst@163.com}Author Vitae ; Sunjie Zhang^cAuthor Vitae ; Wangyan Li^aAuthor Vitae
• 关键词：H&infin ; -consensus filtering ; Sensor network ; Vector dissipativity ; Random parameters ; Event-triggering ; Local performance analysis
• 刊名：Neurocomputing
• 出版年：2017
• 出版时间：5 January 2017
• 年：2017
• 卷：219
• 期：Complete
• 页码：221-231
• 全文大小：1223 K
• 卷排序：219

文摘

This paper investigates the distributed H∞-consensusH∞-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∞-consensusH∞-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.