Full Text:   <2918>

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CLC number: TP13

On-line Access: 2023-05-06

Received: 2022-04-30

Revision Accepted: 2023-05-06

Crosschecked: 2022-08-24

Cited: 0

Clicked: 2248

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yang Cao

https://orcid.org/0000-0002-6940-0868

Stephen AROCKIA SAMY

https://orcid.org/0000-0001-9040-556X

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.4 P.553-566

http://doi.org/10.1631/FITEE.2200181


Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems


Author(s):  Stephen AROCKIA SAMY, Raja RAMACHANDRAN, Pratap ANBALAGAN, Yang CAO

Affiliation(s):  Department of Mathematics, Alagappa University, Karaikudi 630 003, Tamil Nadu, India; more

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

Key Words:  Multi-agent systems (MASs), Non-fragile sampled data control (NFSDC), Time-varying delay, Linear matrix inequality (LMI), Asymptotic synchronization


Stephen AROCKIA SAMY, Raja RAMACHANDRAN, Pratap ANBALAGAN, Yang CAO. Synchronization of nonlinear multi-agent systems using a non-fragile sampled data control approach and its application to circuit systems[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(4): 553-566.

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A1 - Yang CAO
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Abstract: 
The main aim of this work is to design a non-fragile sampled data control (NFSDC) scheme for the asymptotic synchronization criteria for interconnected coupled circuit systems (multi-agent systems, MASs). NFSDC is used to conduct synchronization analysis of the considered MASs in the presence of time-varying delays. By constructing suitable Lyapunov functions, sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to ensure synchronization between the MAS leader and follower systems. Finally, two numerical examples are given to show the effectiveness of the proposed control scheme and less conservation of the proposed Lyapunov functions.

基于非脆弱采样数据控制的非线性多智能体系统同步控制及其在电路系统中的应用

Stephen AROCKIA SAMY1,Raja RAMACHANDRAN2,Pratap ANBALAGAN3,曹阳4
1Alagappa大学数学系,印度泰米尔纳德邦,Karaikudi 630 003
2Alagappa大学高等数学Ramanujan中心,印度泰米尔纳德邦,Karaikudi 630 003
3国立Kunsan大学风能系统研究中心,韩国群山市,573-701
4东南大学网络空间安全学院,中国南京市,210096
摘要:设计了一个非脆弱采样数据控制方案,用于互连耦合电路系统(多智能体系统)的渐近同步标准。该方案对所考虑的多智能体系统在时变延迟情况下作同步分析。通过构建合适的李亚普诺夫函数,得出线性矩阵不等式成立的充分条件,确保多智能体领导者和跟随者系统之间的同步。最后,给出两个数值案例,展示了该控制方案的有效性和所提李亚普诺夫函数的较低保守性。

关键词:多智能体系统;非脆弱采样数据控制;时变延迟;线性矩阵不等式;渐近同步

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

Reference

[1]Ali MS, Agalya R, Saroha S, et al., 2020a. Leaderless consensus of non-linear mixed delay multi-agent systems with random packet losses via sampled-data control. Int J Contr Autom Syst, 18(7):1885-1893.

[2]Ali MS, Agalya R, Shekher V, et al., 2020b. Non-fragile sampled data control for stabilization of non-linear multi-agent system with additive time varying delays, Markovian jump and uncertain parameters. Nonl Anal Hybr Syst, 36:100830.

[3]Alimi AM, Aouiti C, Assali AE, 2019. Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication. Neurocomputing, 332:29-43.

[4]Beard RW, McLain TW, Goodrich MA, et al., 2002. Coordinated target assignment and intercept for unmanned air vehicles. IEEE Trans Robot Autom, 18(6):911-922.

[5]Chen LN, Aihara K, 1995. Chaotic simulated annealing by a neural network model with transient chaos. Neur Netw, 8(6):915-930.

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

[7]Jia Q, Tang WKS, Halang WA, 2011. Leader following of nonlinear agents with switching connective network and coupling delay. IEEE Trans Circ Syst I Reg Papers, 58(10):2508-2519.

[8]Jia Q, Han ZY, Tang WKS, 2019. Synchronization of multi-agent systems with time-varying control and delayed communications. IEEE Trans Circ Syst I Reg Papers, 66(11):4429-4438.

[9]Jiang XL, Xia GH, Feng ZG, et al., 2020. Non-fragile H consensus tracking of nonlinear multi-agent systems with switching topologies and transmission delay via sampled-data control. Inform Sci, 509:210-226.

[10]Kaviarasan B, Kwon OM, Park MJ, et al., 2021. Stochastic faulty estimator-based non-fragile tracking controller for multi-agent systems with communication delay. Appl Math Comput, 392:125704.

[11]Lavanya S, Nagarani S, 2022. Leader-following consensus of multi-agent systems with sampled-data control and looped functionals. Math Comput Simul, 191:120-133.

[12]Liu JL, Yin TT, Yue D, et al., 2021. Event-based secure leader-following consensus control for multiagent systems with multiple cyber attacks. IEEE Trans Cybern, 51(1):162-173.

[13]Liu Y, Tong LY, Lou JG, et al., 2019. Sampled-data control for the synchronization of Boolean control networks. IEEE Trans Cybern, 49(2):726-732.

[14]Lu HT, 2002. Chaotic attractors in delayed neural networks. Phys Lett A, 298(2-3):109-116.

[15]Ma TD, Lewis FL, Song YD, 2016. Exponential synchronization of nonlinear multi-agent systems with time delays and impulsive disturbances. Int J Rob Nonl Contr, 26(8):1615-1631.

[16]Meng ZY, Ren W, Cao YC, et al., 2011. Leaderless and leader-following consensus with communication and input delays under a directed network topology. IEEE Trans Syst Man Cybern B Cybern, 41(1):75-88.

[17]Peng ZN, Luo R, Hu JP, et al., 2022. Distributed optimal tracking control of discrete-time multiagent systems via event-triggered reinforcement learning. IEEE Trans Circ Syst I Reg Papers, 69(6):3689-3700.

[18]Rakkiyappan R, Kaviarasan B, Cao JD, 2015. Leader-following consensus of multi-agent systems via sampled-data control with randomly missing data. Neurocomputing, 161:132-147.

[19]Ren W, Sorensen N, 2008. Distributed coordination architecture for multi-robot formation control. Robot Auton Syst, 56(4):324-333.

[20]Sang H, Zhao J, 2019. Exponential synchronization and L2-gain analysis of delayed chaotic neural networks via intermittent control with actuator saturation. IEEE Trans Neur Netw Learn Syst, 30(12):3722-3734.

[21]Saravanakumar R, Mukaidani H, Amini A, 2020. Non-fragile exponential consensus of nonlinear multi-agent systems via sampled-data control. IFAC-PapersOnLine, 53(2):5677-5682.

[22]Seuret A, Gouaisbaut F, 2013. Wirtinger-based integral inequality: application to time-delay systems. Automatica, 49(9):2860-2866.

[23]Sompolinsky H, Crisanti A, Sommers HJ, 1988. Chaos in random neural networks. Phys Rev Lett, 61(3):259-262.

[24]Subramanian K, Muthukumar P, Joo YH, 2019. Leader-following consensus of nonlinear multi-agent systems via reliable control with time-varying communication delay. Int J Contr Autom Syst, 17(2):298-306.

[25]Tan XG, Cao JD, Li XD, 2019. Consensus of leader-following multiagent systems: a distributed event-triggered impulsive control strategy. IEEE Trans Cybern, 49(3):792-801.

[26]Tang Y, Gao HJ, Zhang WB, et al., 2015. Leader-following consensus of a class of stochastic delayed multi-agent systems with partial mixed impulses. Automatica, 53:346-354.

[27]Wang CY, Zuo ZY, Qi ZQ, et al., 2019. Predictor-based extended-state-observer design for consensus of MASs with delays and disturbances. IEEE Trans Cybern, 49(4):1259-1269.

[28]Wang TC, He X, Huang TW, 2016. Complex dynamical behavior of neural networks in circuit implementation. Neurocomputing, 190:95-106.

[29]Wang WP, Jia X, Luo X, et al., 2019. Fixed-time synchronization control of memristive MAM neural networks with mixed delays and application in chaotic secure communication. Chaos Sol Fract, 126:85-96.

[30]Wang YL, Cao JD, Hu JQ, 2014. Pinning consensus for multi-agent systems with non-linear dynamics and time-varying delay under directed switching topology. IET Contr Theory Appl, 8(17):1931-1939.

[31]Xu XY, Li WQ, Wang MQ, 2021. Distributed output tracking of nonlinear multi-agent systems by linear sampled-data control. Neurocomputing, 462:238-246.

[32]Yang J, Zhong QS, Shi KB, et al., 2022. Co-design of observer-based fault detection filter and dynamic event-triggered controller for wind power system under dual alterable DoS attacks. IEEE Trans Inform Forens Secur, 17:1270-1284.

[33]Yue DD, Cao JD, Li Q, et al., 2021. Neural-network-based fully distributed adaptive consensus for a class of uncertain multiagent systems. IEEE Trans Neur Netw Learn Syst, 32(7):2965-2977.

[34]Zhang D, Xu ZH, Karimi HR, et al., 2018. Distributed H output-feedback control for consensus of heterogeneous linear multiagent systems with aperiodic sampled-data communications. IEEE Trans Ind Electron, 65(5):4145-4155.

[35]Zhao C, Liu XZ, Zhong SM, et al., 2021. Leader-following consensus of multi-agent systems via novel sampled-data event-triggered control. Appl Math Comput, 395:125850.

[36]Zhao YS, Li XD, Duan PY, 2019. Observer-based sliding mode control for synchronization of delayed chaotic neural networks with unknown disturbance. Neur Netw, 117:268-273.

[37]Zheng MW, Li LX, Peng HP, et al., 2017. Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method. Neurocomputing, 238:67-75.

[38]Zhong QS, Yang J, Shi KB, et al., 2022. Event-triggered H load frequency control for multi-area nonlinear power systems based on non-fragile proportional integral control strategy. IEEE Trans Intell Transp Syst, 23(8):12191-12201.

[39]Zhou B, Liao XF, Huang TW, et al., 2015. Leader-following exponential consensus of general linear multi-agent systems via event-triggered control with combinational measurements. Appl Math Lett, 40:35-39.

[40]Zhu B, Zhong QS, Chen YS, et al., 2022. A novel reconstruction method for temperature distribution measurement based on ultrasonic tomography. IEEE Trans Ultras Ferroelectr Freq Contr, 69(7):2352-2370.

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