2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

具有拓扑切换特性的离散型不确定时空网络的指数同步

韩昌辉 葛连珺 高丽宇 吕翎

韩昌辉, 葛连珺, 高丽宇, 吕翎. 具有拓扑切换特性的离散型不确定时空网络的指数同步.自动化学报, 2021, 47(3): 706-714 doi: 10.16383/j.aas.c180575
引用本文: 韩昌辉, 葛连珺, 高丽宇, 吕翎. 具有拓扑切换特性的离散型不确定时空网络的指数同步.自动化学报, 2021, 47(3): 706-714 doi: 10.16383/j.aas.c180575
Han Chang-Hui, Ge Lian-Jun, Gao Li-Yu, Lv Ling. Exponential synchronization of discrete uncertain spatiotemporal networks with topology switching characteristics. Acta Automatica Sinica, 2021, 47(3): 706-714 doi: 10.16383/j.aas.c180575
Citation: Han Chang-Hui, Ge Lian-Jun, Gao Li-Yu, Lv Ling. Exponential synchronization of discrete uncertain spatiotemporal networks with topology switching characteristics. Acta Automatica Sinica, 2021, 47(3): 706-714 doi: 10.16383/j.aas.c180575

具有拓扑切换特性的离散型不确定时空网络的指数同步

doi: 10.16383/j.aas.c180575
基金项目: 

国家自然科学基金 11747318

详细信息
    作者简介:

    韩昌辉  辽宁师范大学物理与电子技术学院硕士研究生. 2016年获得辽宁师范大学学士学位. 主要研究方向为复杂网络的变结构控制与网络同步. E-mail: lnnuhch@163.com

    葛连珺  辽宁师范大学物理与电子技术学院硕士研究生. 2016年获得渤海大学学士学位. 主要研究方向为复杂网络的开环闭环控制与同步. E-mail: 18742068500@163.com

    高丽宇  辽宁师范大学物理与电子技术学院硕士研究生. 2016年获得鞍山师范学院学士学位. 主要研究方向为复杂网络的滑模控制与网络同步. E-mail: 18742050326@163.com

    通讯作者:

    吕翎  辽宁师范大学物理与电子技术学院教授. 主要研究方向为复杂网络的同步控制与参数估计. 本文通信作者. E-mail: luling1960@aliyun.com

  • 本文责任编委 张卫东

Exponential Synchronization of Discrete Uncertain Spatiotemporal Networks With Topology Switching Characteristics

Funds: 

National Natural Science Foundation of China 11747318

More Information
    Author Bio:

    HAN Chang-Hui  Master student at the School of Physics and Electronic Technology, Liaoning Normal University. He received his bachelor degree from Liaoning Normal University in 2016. His research interest covers variable structure control of complex network and network synchronization

    GE Lian-Jun  Master student at the School of Physics and Electronic Technology, Liaoning Normal University. She received her bachelor degree from Bohai University in 2016. Her research interest covers open-plus-closed-loop control and synchronization in complex network

    GAO Li-Yu  Master student at the School of Physics and Electronic Technology, Liaoning Normal University. She received her bachelor degree from Anshan Normal College in 2016. Her research interest covers sliding mode control of complex network and network synchronization

    Corresponding author: LV Ling  Professor at the School of Physics and Electronic Technology, Liaoning Normal University. Her research interest covers synchronization control and parameter estimation in complex network. Corresponding author of this paper
  • Recommended by Associate Editor ZHANG Wei-Dong
  • 摘要: 研究了具有拓扑切换特性的离散型不确定时空网络的指数同步问题. 基于稳定性理论, 构造了具有指数形式的Lyapunov函数, 并设计了同步控制器的结构方程, 进而获得了时空网络的同步条件. 同时, 我们设计了未知参数的识别律, 有效地识别了网络中的未知参数. 最后, 选取实际的激光相位共轭波空间扩展系统作为网络节点进行仿真模拟, 验证了同步方案的可行性与控制器的有效性. 通过构造具有指数形式的Lyapunov函数, 能够有效地调节网络的同步速率. 并且获得的同步条件中不包含网络的耦合矩阵项, 消除了拓扑切换特性对同步过程的影响, 使得网络同步性能更加稳定.
    Recommended by Associate Editor ZHANG Wei-Dong
    1)  本文责任编委 张卫东
  • 图  1  状态变量$x(m, n)$的时空演化

    Fig.  1  Spatiotemporal evolution of state variable $x(m, n)$

    图  2  拓扑切换信号$s(n)$

    Fig.  2  The topology switching signal $s(n)$

    图  3  4种切换拓扑结构图

    Fig.  3  Four switching topologies

    图  4  误差$e_1(m, n)$随时空的演化

    Fig.  4  Spatiotemporal evolution of error $e_1(m, n)$

    图  5  误差$e_2(m, n)$随时空的演化

    Fig.  5  Spatiotemporal evolution of error $e_2(m, n)$

    图  6  误差$e_3\, (m, n)$随时空的演化

    Fig.  6  Spatiotemporal evolution of error $e_3\, (m, n)$

    图  7  误差$e_4(m, n)$随时空的演化

    Fig.  7  Spatiotemporal evolution of error $e_4(m, n)$

    图  8  误差$e_5(m, n)$随时空的演化

    Fig.  8  Spatiotemporal evolution of error $e_5(m, n)$

    图  9  误差$e_6(m, n)$随时空的演化

    Fig.  9  Spatiotemporal evolution of error $e_6(m, n)$

    图  10  误差$e_7(m, n)$随时空的演化

    Fig.  10  Spatiotemporal evolution of error $e_7(m, n)$

    图  11  未知参数$\varepsilon_1(m, n)$随时空的演化

    Fig.  11  Spatiotemporal evolution of unknown parameter $\varepsilon_1(m, n)$

    图  12  未知参数$\varepsilon_2(m, n)$随时空的演化

    Fig.  12  Spatiotemporal evolution of unknown parameter $\varepsilon_2(m, n)$

    图  13  未知参数$\varepsilon_3\, (m, n)$随时空的演化

    Fig.  13  Spatiotemporal evolution of unknown parameter $\varepsilon_3\, (m, n)$

    图  14  未知参数$\varepsilon_4(m, n)$随时空的演化

    Fig.  14  Spatiotemporal evolution of unknown parameter $\varepsilon_4(m, n)$

    图  15  未知参数$\varepsilon_5(m, n)$随时空的演化

    Fig.  15  Spatiotemporal evolution of unknown parameter $\varepsilon_5(m, n)$

    图  16  未知参数$\varepsilon_6(m, n)$随时空的演化

    Fig.  16  Spatiotemporal evolution of unknown parameter $\varepsilon_6(m, n)$

    图  17  未知参数$\varepsilon_7(m, n)$随时空的演化

    Fig.  17  Spatiotemporal evolution of unknown parameter $\varepsilon_7(m, n)$

  • [1] Xu X F, Zong G D, Hou L L. Passivity-based stabilization and passive synchronization of complex nonlinear networks. Neurocomputing, 2016, 175: 101-109 doi: 10.1016/j.neucom.2015.10.040
    [2] Wang F, Yang Y Q, Hu M F, Xu X Y. Projective cluster synchronization of fractional-order coupled-delay complex network via adaptive pinning control. Physica A, 2015, 434: 134-143 doi: 10.1016/j.physa.2015.03.089
    [3] Xu Y H, Zhou W N, Fang J A, Xie C R, Tong D B. Finite-time synchronization of the complex dynamical network with non-derivative and derivative coupling. Neurocomputing, 2016, 173: 1356-1361 doi: 10.1016/j.neucom.2015.09.008
    [4] Wu H Q, Wang L F, Niu P F, Wang Y. Global projective synchronization in finite time of nonidentical fractionalorder neural networks based on sliding mode control strategy. Neurocomputing, 2017, 235: 264-273 doi: 10.1016/j.neucom.2017.01.022
    [5] Anbuvithya R, Mathiyalagan K, Sakthivel R, PrakashP. Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations. Communications in Nonlinear Science and Numerical Simulation, 2015, 29: 427-440 doi: 10.1016/j.cnsns.2015.05.020
    [6] Yang Y, Wang Y, Li T Z. Outer synchronization of fractional-order complex dynamical networks. Optik, 2016, 127: 7395-7407 doi: 10.1016/j.ijleo.2016.05.029
    [7] Wang S G, Zheng S, Zhang B W, Cao H T. Modified function projective lag synchronization of uncertaincomplex networks with time-varying coupling strength. Optik, 2016, 127: 4716-4725 doi: 10.1016/j.ijleo.2016.01.085
    [8] Chandrasekar A, Rakkiyappan R. Impulsive controller design for exponential synchronization of delayed stochastic memristor-based recurrent neural networks. Neurocomputing, 2016, 173: 1348-1355 doi: 10.1016/j.neucom.2015.08.088
    [9] Srinivasan K, Chandrasekar V K, Gladwin P R, Murali K, Lakshmanan M. Different types of synchronization in coupled network based chaotic circuits. Communications in Nonlinear Science and Numerical Simulation, 2016, 39: 156-168 doi: 10.1016/j.cnsns.2016.03.002
    [10] Zhai S D. Modulus synchronization in a network of nonlinear systems with antagonistic interactions and switching topologies. Communications in Nonlinear Science and Numerical Simulation, 2016, 33: 184-193 doi: 10.1016/j.cnsns.2015.09.010
    [11] Wang X, Yang G H. Distributed H$_\infty$ consensus tracking control for multi-agent networks with switching directed topologies. Neurocomputing, 2016, 207: 693-699 doi: 10.1016/j.neucom.2016.05.052
    [12] Fan J B, Wang Z X, Jiang G P. Quasi-synchronization of heterogeneous complex networks with switching sequentially disconnected topology. Neurocomputing, 2017, 237: 342-349 doi: 10.1016/j.neucom.2017.01.025
    [13] Dai A D, Zhou W N, Xu Y H, Xiao C. Adaptive exponential synchronization in mean square for Markovian jumping neutral-type coupled neural networks with time-varying delays by pinning control. Neurocomputing, 2016, 173: 809-818 doi: 10.1016/j.neucom.2015.08.034
    [14] Zhang Q J, Chen G R, Wan L. Exponential synchronization of discrete-time impulsive dynamical networks with time-varying delays and stochastic disturbances. Neurocomputing, 2018, 309: 62-69 doi: 10.1016/j.neucom.2018.04.070
    [15] Sakthivel R, Sathishkumar M, Kaviarasan B, Marshal Anthoni S. Synchronization and state estimation for stochastic complex networks with uncertain inner coupling. Neurocomputing, 2017, 238: 44-45 doi: 10.1016/j.neucom.2017.01.035
    [16] Cheng R R, Peng M S, Zuo J. Pinning synchronization of discrete dynamical networks with delay coupling. Physica A, 2016, 450: 444-453 doi: 10.1016/j.physa.2016.01.004
    [17] Mohammadzadeh A, Ghaemi S. A modified sliding mode approach for synchronization of fractional-order chaotic/hype- rchaotic systems by using new self-structuring hierarchical type-2 fuzzy neural network. Neurocomputing, 2016, 191: 200-213 doi: 10.1016/j.neucom.2015.12.098
    [18] Zhao L, Jia Y M. Neural network-based distributed adaptive attitude synchronization control of spacecraft formation under modified fast terminal sliding mode. Neurocomputing, 2016, 171: 230-241 doi: 10.1016/j.neucom.2015.06.063
    [19] Yang L X, Jiang J, Liu X J. Synchronization of fractional-order colored dynamical networks via open-plus-closed-loop control. Physica A, 2016, 443: 200-211 doi: 10.1016/j.physa.2015.09.062
    [20] Fan Y Q, Xing K Y, Wang Y H, Wang L Y. Projective synchronization adaptive control for different chaoticneural networks with mixed time delays. Optik, 2016, 127: 2551-2557 doi: 10.1016/j.ijleo.2015.11.227
    [21] Li J M, He C, Zhang W Y, Chen M L. Adaptive synchronization of delayed reaction-diffusion neural networks with unknown non-identical time-varying coupling strengths. Neurocomputing, 2017, 219: 144-153 doi: 10.1016/j.neucom.2016.09.006
    [22] Ahmed M A A, Liu Y R, Zhang W B, Alsaadi F E. Exponential synchronization via pinning adaptive control for complex networks of networks with time delays. Neurocomputing, 2017, 225: 198-204 doi: 10.1016/j.neucom.2016.11.022
    [23] Xu Q, Zhuang S X, Liu S J, Xiao J. Decentralized adaptive coupling synchronization of fractional-order complex-vari- able dynamical networks. Neurocomputing, 2016, 186: 119-126 doi: 10.1016/j.neucom.2015.12.072
    [24] Han X M, Wu H Q, Fang B L. Adaptive exponential synchronization of memristive neural networks with mixed time-varying delays. Neurocomputing, 2016, 201: 40-50 doi: 10.1016/j.neucom.2015.11.103
    [25] Ahmed M A A, Liu Y R, Zhang W B, Alsaedi A, Hayat T. Exponential synchronization for a class of complex networks of networks with directed topology and time delay. Neurocomputing, 2017, 266: 274-283 doi: 10.1016/j.neucom.2017.05.039
    [26] Dai H, Chen W S, Jia J P, Liu J Y, Zhang Z Q. Exponential synchronization of complex dynamical networks with time-varying inner coupling via event-triggered communication. Robotics and Autonomous Systems, 2017, 245: 124-132
    [27] Beli M R, Stojkov P. Chaos in phase-conjugate resonators as a multimodal mapping. Optical and Quantum Electronics, 1990, 22: 157-165 doi: 10.1007/BF02189951
    [28] Kaneko K. Spatial period-doubling in open flow. Physics Letters A, 1980, 111: 321-325 http://www.sciencedirect.com/science/article/pii/0375960185903597
  • 加载中
图(17)
计量
  • 文章访问数:  58
  • HTML全文浏览量:  98
  • PDF下载量:  138
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-08-28
  • 录用日期:  2019-02-15
  • 刊出日期:  2021-04-02

目录

    /

    返回文章
    返回