Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

doi:10.1088/1674-1056/ad20d7

Abstract In many engineering networks, only a part of target state variables are required to be estimated. On the other hand, multi-layer complex network exists widely in practical situations. In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied. A suitable functional state observer is constructed with the limited measurement. The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem. Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained. Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states. Thus, it can greatly reduce the placement of observers and computational cost. Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.

Keywords: multi-layer complex dynamical network nonlinear node dynamics target state estimation functional state observer

Received: 01 November 2023
Revised: 21 December 2023
Accepted manuscript online: 22 January 2024

PACS:	02.30.Yy	(Control theory)
	07.05.Dz	(Control systems)
	05.45.Xt	(Synchronization; coupled oscillators)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 62373197 and 61873326).

Corresponding Authors: Guo-Ping Jiang
E-mail: jianggp@njupt.edu.cn

Cite this article:

Yayong Wu(吴亚勇), Xinwei Wang(王欣伟), and Guo-Ping Jiang(蒋国平) Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics 2024 Chin. Phys. B 33 040205

[1] Wang X, Wang X F, Su H and Jam L 2019 Physica A 533 122028
[5] Cai Y, Cao Y, Li Y, Huang T and Zhou B 2016 IEEE Trans. Smart Grid 7 530
[6] Lai Q and Zhang H 2022 Chin. Phys. B 31 068905
[7] Li N, Sun H, Jing X and Chen Z 2021 Chin. Phys. B 30 090507
[8] Giorgio B and Luigi C 2016 Automatica 68 169
[9] Li W, Jia Y and Du J 2017 IEEE Trans. Automat. Contr. 62 6377
[10] Li W, Jia Y and Du J 2017 Neurocomputing 219 1
[11] Wu X, Jiang G P and Wang X 2018 IEEE Trans. Circuits Syst. II 65 1753
[12] Zhang M, Wang X J, Jin L, Song M and Wang X 2020 Chin. Phys. B 29 096401
[13] Zhang Y, Hong Y, Mohsen G, Wu S, Zhang P and Liu R 2023 IEEE Trans. Veh. Technol. 10 1
[14] Xiao Y, Sun Z, Shi G and Dusit N 2023 IEEE J. Area. Comm. 41 639
[15] Li W, Zhou J, Li J, Xie T and Lu J A 2021 IEEE Trans. Circuits Syst. II 68 1338
[16] Jin Y L, Han Q Y, Guo R Z, Gao Y and Shen L Q 2023 Chin. Phys. B 32 100507
[17] Wang Y, Wu X, Lv J H, Lu J A and Raissa M D S 2020 IEEE Trans. Netw. Sci. Eng. 7 538
[18] Mei G, Wu X, Wang Y, Hu M, Lu J A and Chen G R 2018 IEEE Trans. Cybern. 48 754
[19] Wu J, Li Y and Chen G R 2020 IEEE Trans. Circuits Syst. I 67 5211
[20] Jiang G P, Li K and Wang X 2021 33rd Chinese Control and Decision Conference, May 22——24, 2021, Kunming, China
[21] Li K, Wang X and Jiang G P 2021 40th Chinese Control Conference, July 26——28, 2021, Shanghai, China
[22] Xiong F, Liu Y and Cheng J 2017 Commun. Nonlinear Sci. 44 513
[23] Motter A E 2015 Chaos 25 097621
[24] Darouach M 2000 IEEE Trans. Automat. Contr. 45 940
[25] Trinh H and Fernando T 2012 Functional Observers for Dynamical Systems (Berlin, Heidelberg:Springer Verlag)
[26] Liu Y Y, Slotine J J and Barabasi A L 2011 Nature 473 167
[27] Arthur N M, Duan C, Aguirre L A and Motter A E 2021 Proc. Natl. Acad, Sci. USA 119 e2113750119
[28] Wu Y, Wang X, Jiang G P and Gu M 2023 J. Franklin I. 360 8178
[29] Wang L, Lu D, Zhang Y and Wang X 2018 Sensors 18 3434
[30] Matthew P A, Scott A S, Kate J H, Christopher M B, Matthew H H, Michaela P, Jacinta H, Kerrie L M and Eve M M 2020 Ecol. Lett. 23 607
[31] Yang H, Tang W K S, Chen G R and Jiang G P 2017 IEEE Trans. Circuits Syst. I 64 2182
[32] Stephen B, Laurent El G, Eric F and Venkataramanan B 1994 Linear Matrix Inequalities in System and Control Theory (Philadelphia:Society for Industrial and Applied Mathematics)

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Target layer state estimation in multi-layer complex dynamical networks considering nonlinear node dynamics

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