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• W4225417814 abstract "This paper deals with the privacy-preserving average consensus problem for continuous-time multi-agent network systems (MANSs) based on the event-triggered strategy. A novel event-triggered privacy-preserving consensus algorithm is designed to achieve the average consensus of MANSs while avoiding the disclosure of the agents’ initial states. Different from the approaches incorporating stochastic noises, an output mask function in the proposed algorithm is developed to make initial state of each agent indiscernible by the others. Particularly, under the output mask function, all agents can exactly tend to the average value of initial states rather than the mean square value. Under the proposed algorithm, detailed theoretical proof about average consensus and privacy of the MANSs are conducted. Moreover, the proposed algorithm is extended to nonlinear continuous-time MANSs, and the corresponding results are also derived. A numerical simulation eventually is performed to demonstrate the validity of our results." @default.
• W4225417814 created "2022-05-05" @default.
• W4225417814 creator A5020438835 @default.
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• W4225417814 date "2022-07-01" @default.
• W4225417814 modified "2023-10-18" @default.
• W4225417814 title "Event-Triggered privacy-preserving average consensus for continuous-time multi-agent network systems" @default.
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• W4225417814 doi "https://doi.org/10.1016/j.jfranklin.2022.04.028" @default.
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