Dynamic event-triggered resilient state estimation for time-varying complex networks with Markovian switching topologies
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Published on: May 29, 2017
Xianye Bu1, Jinbo Song1, Fengcai Huo2
1School of Electrical Engineering and Information, Northeast Petroleum University, Daqing 163318, China; Artificial Intelligence Energy Research Institute of Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Daqing 163318, China; SANYA Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572024, China.
Abstract:
This paper addresses a resilient state estimation problem for an array of nonlinear complex networks with switching topologies under the dynamic event-triggered mechanism (ETM). To reduce the unnecessary data delivery, the dynamic ETM is introduced to schedule the data delivery from sensors to estimators. The model of the switched complex networks is established by adopting a Markov chain which is better to reflect the characteristics of practical complex networks. A set of novel estimators is obtained by using the properties of Kronecker product combining with the Lyapunov-Krasovskii method, and some easy-to-check conditions are derived such that the dynamics of state estimation error satisfies the prescribed H∞ performance index. In addition, the parameters of the designed resilient state estimators can be acquired by solving a series of convex optimization problems. In the end, a simulation example is given to demonstrate the validity of the proposed theoretical results in this paper.
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