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Scalable consensus filtering for uncertain systems over sensor networks with Round‐Robin protocol
Author(s) -
Han Fei,
Wang Zidong,
Chen Guanrong,
Dong Hongli
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5327
Subject(s) - scalability , computer science , filtering problem , node (physics) , protocol (science) , bounded function , norm (philosophy) , wireless sensor network , filter (signal processing) , distributed computing , control theory (sociology) , mathematical optimization , filter design , mathematics , computer network , engineering , control (management) , medicine , alternative medicine , structural engineering , pathology , database , artificial intelligence , law , political science , mathematical analysis , computer vision
This article is concerned with the scalable distributed H ∞ ‐consensus filtering problem for a class of discrete time‐varying systems over sensor networks with the Round‐Robin protocol (RRP). The challenge comes from the fact that the time‐varying parameters of the network are subject to randomly occurring norm‐bounded uncertainties and the measurement outputs of the sensor nodes are saturated due to the sector nonlinearities. For preventing data collisions and saving energy, the RRP determines which neighboring node can access the shared network for information transmission at each time step. An H ∞ performance index is proposed to characterize the disturbance attenuation level of the resulting filtering error dynamics. By stochastic analysis in combination with the recursive matrix inequality approach, a distributed filtering algorithm is developed for each individual sensor node to ensure the prespecified estimation performance. Finally, an illustrative simulation example is shown to verify the effectiveness and applicability of the theoretical results.