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On-line Access: 2021-01-11

Received: 2020-04-08

Revision Accepted: 2020-06-01

Crosschecked: 2020-08-06

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Citations:  Bibtex RefMan EndNote GB/T7714


Qing-ling Wang


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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.1 P.88-96


Convergence of time-varying networks and its applications

Author(s):  Qingling Wang

Affiliation(s):  School of Automation, Southeast University, Nanjing 210096, China

Corresponding email(s):   qlwang@seu.edu.cn

Key Words:  Time-varying networks, Unknown control directions, Nussbaum-type function, Cut-balance condition

Qingling Wang. Convergence of time-varying networks and its applications[J]. Frontiers of Information Technology & Electronic Engineering, 2021, 22(1): 88-96.

@article{title="Convergence of time-varying networks and its applications",
author="Qingling Wang",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Convergence of time-varying networks and its applications
%A Qingling Wang
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 1
%P 88-96
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000160

T1 - Convergence of time-varying networks and its applications
A1 - Qingling Wang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 1
SP - 88
EP - 96
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000160

In this study, we present the convergence of time-varying networks. Then, we apply the convergence property to cooperative control of nonlinear multiagent systems (MASs) with unknown control directions (UCDs), and illustrate a new kind of nussbaum-type function based control algorithms. It is proven that if the time-varying networks are cut-balance, the convergence of nonlinear MASs with nonidentical UCDs is achieved using the presented algorithms. A critical feature of this application is that the designed algorithms can deal with nonidentical UCDs by employing conventional nussbaum-type functions. Finally, one simulation example is given to illustrate the effectiveness of the presented algorithms.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Fax JA, Murray RM, 2004. Information flow and cooperative control of vehicle formations. IEEE Trans Autom Contr, 49(9):1465-1476.

[2]Ge SS, Hong F, Lee TH, 2004. Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. IEEE Trans Syst Man Cybern Part B (Cybern), 34(1):499-516.

[3]Hendrickx JM, Tsitsiklis JN, 2013. Convergence of type-symmetric and cut-balanced consensus seeking systems. IEEE Trans Autom Contr, 58(1):214-218.

[4]Hong YG, Hu JP, Gao LX, 2006. Tracking control for multi-agent consensus with an active leader and variable topology. Automatica, 42(7):1177-1182.

[5]Li ZK, Duan ZS, Chen GR, et al., 2010. Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint. IEEE Trans Circ Syst I, 57(1):213-224.

[6]Moreau L, 2005. Stability of multiagent systems with time-dependent communication links. IEEE Trans Autom Contr, 50(2):169-182.

[7]Nussbaum RD, 1983. Some remarks on a conjecture in parameter adaptive control. Syst Contr Lett, 3(5):243-246.

[8]Olfati-Saber R, Murray RM, 2004. Consensus problems in networks of agents with switching topology and time-delays. IEEE Trans Autom Contr, 49(9):1520-1533.

[9]Park JH, Kwon OM, Lee SM, 2008. LMI optimization approach to stabilization of Genesio-Tesi chaotic system via dynamic controller. Appl Math Comput, 196(1):200-206.

[10]Peng JM, Ye XD, 2014. Cooperative control of multiple heterogeneous agents with unknown high-frequency-gain signs. Syst Contr Lett, 68:51-56.

[11]Polycarpou MM, Ioannou PA, 1996. A robust adaptive nonlinear control design. Automatica, 32(3):423-427.

[12]Psillakis HE, 2017. Consensus in networks of agents with unknown high-frequency gain signs and switching topology. IEEE Trans Autom Contr, 62(8):3993-3998.

[13]Shahnazi R, Wang W, 2018. Distributed adaptive FBC of uncertain nonaffine multiagent systems preceded by unknown input nonlinearities with unknown gain sign. IEEE Trans Syst Man Cybern Syst, 50(8):3036-3046.

[14]Song YD, Wang YJ, Wen CY, 2017. Adaptive fault-tolerant PI tracking control with guaranteed transient and steady-state performance. IEEE Trans Autom Contr, 62(1):481-487.

[15]Su SZ, Lin ZL, 2016. Distributed consensus control of multi-agent systems with higher order agent dynamics and dynamically changing directed interaction topologies. IEEE Trans Autom Contr, 61(2):515-519.

[16]Wang CL, Wen CY, Lin Y, 2017. Adaptive actuator failure compensation of a class of nonlinear systems with unknown control direction. IEEE Trans Autom Contr, 62(1):385-392.

[17]Wang G, 2019. Distributed control of higher-order nonlinear multi-agent systems with unknown non-identical control directions under general directed graphs. Automatica, 110:108559.

[18]Wang QL, Sun CY, 2020. Adaptive consensus of multiagent systems with unknown high-frequency gain signs under directed graphs. IEEE Trans Sys Man Cybern Syst, 50(6):2181-2186.

[19]Wang QL, Psillakis HE, Sun CY, 2019a. Adaptive cooperative control with guaranteed convergence in time-varying networks of nonlinear dynamical systems. IEEE Trans Cybern, in press.

[20]Wang QL, Psillakis HE, Sun CY, 2019b. Cooperative control of multiple agents with unknown high-frequency gain signs under unbalanced and switching topologies. IEEE Trans Autom Contr, 64(6):2495-2501.

[21]Wang QL, Psillakis HE, Sun CY, 2019c. Cooperative control of multiple high-order agents with nonidentical unknown control directions under fixed and time-varying topologies. IEEE Trans Syst Man Cybern Syst, in press.

[22]Wen GH, Zheng WX, 2019. On constructing multiple Lyapunov functions for tracking control of multiple agents with switching topologies. IEEE Trans Autom Contr, 64(9):3796-3803.

[23]Wen GH, Duan ZS, Chen GR, et al., 2014. Consensus tracking of multi-agent systems with Lipschitz-type node dynamics and switching topologies. IEEE Trans Circ Syst I, 61(2):499-511.

[24]Yu WW, Chen GR, Cao M, 2010. Some necessary and sufficient conditions for second-order consensus in multi-agent dynamical systems. Automatica, 46(6):1089-1095.

[25]Yu ZX, Li SG, Yu ZS, et al., 2018. Adaptive neural output feedback control for nonstrict-feedback stochastic nonlinear systems with unknown backlash-like hysteresis and unknown control directions. IEEE Trans Neur Netw Learn Syst, 29(4):1147-1160.

[26]Zhang Y, Wen CY, Soh YC, 2000. Adaptive backstepping control design for systems with unknown high-frequency gain. IEEE Trans Autom Contr, 45(12):2350-2354.

[27]Zheng Y, Zhu Y, Wang L, 2011. Consensus of heterogeneous multi-agent systems. IET Contr Theory Appl, 5(16):1881-1888.

[28]Zheng YF, Wen CY, Li ZG, 2013. Robust adaptive asymptotic tracking control of uncertain nonlinear systems subject to nonsmooth actuator nonlinearities. Int J Adapt Contr Signal Process, 27(1-2):108-121.

[29]Zheng YS, Wang L, 2012. Finite-time consensus of heterogeneous multi-agent systems with and without velocity measurements. Syst Contr Lett, 61(8):871-878.

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