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\(\pmb {H_\infty }\) fusion estimation of time-delayed nonlinear systems with energy constraints: the finite-horizon case

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Abstract

The fusion estimation issue of sensor networks is investigated for nonlinear time-varying systems with energy constraints, time delays as well as packet loss. For the addressed problem, some local estimations are first obtained by using the designed Luenberger-type local estimator and then transmitted to a fusion center (FC) to generate a desired fusion value. A novel transmission model with energy constraints is proposed, where part information is reliably transmitted and the other is randomly determined whether to be transmitted. Furthermore, a diagonal matrix is utilized to describe the communication scheduling. With the help of the Lyapunov stability theory, sufficient conditions are established to ensure the predetermined local and fused \(H_{\infty }\) performances over a finite horizon. Furthermore, by virtue of the well-known Schur complement lemma, the desired gains of local estimators and the suboptimal fusion weight matrices are obtained in light of the solution of linear matrix inequalities. It should be pointed out that the developed scheme is a two-step process under which the design of fusion weight matrices is based on the obtained estimator gains. Finally, a simulation example for sensor networks is performed to check the effectiveness of the proposed fusion scheme.

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Funding

This work was supported in part by the National Natural Science Foundation of China under Grants 61973219, 61933007, and 61772018.

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Contributions

Derui Ding and Xiaojian Yi contributed to conceptualization; Guoliang Wei and Meiling Xie contributed to methodology; Meiling Xie and Xiaojian Yi contributed to formal analysis and investigation; Meiling Xie contributed to writing—original draft preparation.

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Correspondence to Derui Ding.

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Xie, M., Ding, D., Wei, G. et al. \(\pmb {H_\infty }\) fusion estimation of time-delayed nonlinear systems with energy constraints: the finite-horizon case. Nonlinear Dyn 107, 2583–2598 (2022). https://doi.org/10.1007/s11071-021-07098-4

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  • DOI: https://doi.org/10.1007/s11071-021-07098-4

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