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dc.contributor.authorHu, J-
dc.contributor.authorWang, Z-
dc.contributor.authorLiang, J-
dc.contributor.authorDong, H-
dc.identifier.citationJournal of the Franklin Institute, 352(9): 3750 - 3763, (2015)en_US
dc.description.abstractThis paper is concerned with the problem of event-triggered distributed state estimation for a class of discrete nonlinear stochastic systems with time-varying delays, randomly occurring uncertainties and randomly occurring nonlinearities. Both the uncertainties and nonlinearities enter into the system in a random way characterized by random variables obeying the Bernoulli distribution. An event-triggered scheme is introduced to reduce the number of excessive executions of the signal transmissions. The aim of this paper is to design a distributed state estimator such that the estimation error dynamics is asymptotically mean-square stable. By constructing a Lyapunov-Krasovskii functional and employing the delay-fractioning approach, sufficient conditions are established to guarantee the desired performance requirements and then the explicit form of the distributed estimator gains is parameterized. An illustrative example is finally provided to demonstrate the effectiveness of the developed distributed state estimation scheme with the event-triggered communication mechanism.en_US
dc.description.sponsorshipThis work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61134009, 11301118, 61174136 and 61374127, the China Postdoctoral Science Foundation under Grant 2014M560376, Jiangsu Planned Projects for Postdoctoral Research Funds under Grant 1402004A, and the Alexander von Humboldt Foundation of Germany.en_US
dc.format.extent3750 - 3763-
dc.subjectSensor networksen_US
dc.subjectDistributed state estimationen_US
dc.subjectRandomly occurring uncertaintiesen_US
dc.subjectRandomly occurring nonlinearitiesen_US
dc.subjectTime-varying delayen_US
dc.titleEvent-triggered distributed state estimation with randomly occurring uncertainties and nonlinearities over sensor networks: A delay-fractioning approachen_US
dc.relation.isPartOfJournal of the Franklin Institute-
Appears in Collections:Dept of Computer Science Research Papers

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