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Dynamic event-triggered resilient state estimation for time-varying complex networks with Markovian switching

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.

ISA Transactions
|June 6, 2022
PubMed
Summary

This study introduces a dynamic event-triggered mechanism (ETM) for resilient state estimation in nonlinear complex networks. This approach optimizes data delivery while ensuring performance guarantees for switching network topologies.

Keywords:
Complex networksDynamic event-triggered mechanismResilient state estimationSwitching topologies

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