IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v335y2018icp38-49.html
   My bibliography  Save this article

Output synchronization control for a class of complex dynamical networks with non-identical dynamics

Author

Listed:
  • Xiao, Xiang
  • Li, Xiao-Jian
  • Jin, Xiao-Zheng
  • Cui, Yan

Abstract

This paper is concerned with the output synchronization control problem for a class of complex dynamical networks (CDNs) with non-identical dynamics. An output feedback control protocol consisting of a feedforward control law and a feedback control law is developed to achieve the output synchronization. More specifically, the feedforward control law is designed to compensate the coupling dynamics of the CDNs, and the feedback control law is designed by solving an algebraic Riccati equation (ARE), which is established by defining a modified quadratic performance index. It is shown that the output feedback control protocol solves the output synchronization control problem, and the graph theory is used to construct a global Lyapunov function, based on which a rigorous asymptotic convergence analysis of output synchronization errors is conducted. Finally, a simulation example is given to verify the effectiveness of the theoretical results.

Suggested Citation

  • Xiao, Xiang & Li, Xiao-Jian & Jin, Xiao-Zheng & Cui, Yan, 2018. "Output synchronization control for a class of complex dynamical networks with non-identical dynamics," Applied Mathematics and Computation, Elsevier, vol. 335(C), pages 38-49.
  • Handle: RePEc:eee:apmaco:v:335:y:2018:i:c:p:38-49
    DOI: 10.1016/j.amc.2018.04.029
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300318303473
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2018.04.029?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bao, Haibo & Park, Ju H. & Cao, Jinde, 2015. "Matrix measure strategies for exponential synchronization and anti-synchronization of memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 543-556.
    2. Chen, Xiangyong & Park, Ju H. & Cao, Jinde & Qiu, Jianlong, 2017. "Sliding mode synchronization of multiple chaotic systems with uncertainties and disturbances," Applied Mathematics and Computation, Elsevier, vol. 308(C), pages 161-173.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Fei & Yang, Yongqing, 2018. "Quasi-synchronization for fractional-order delayed dynamical networks with heterogeneous nodes," Applied Mathematics and Computation, Elsevier, vol. 339(C), pages 1-14.
    2. Liu, Cheng-Qian & Li, Xiao-Jian & Long, Yue & Sun, Jie, 2020. "Output feedback secure control for cyber-physical systems against sparse sensor attacks," Applied Mathematics and Computation, Elsevier, vol. 384(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zeng, Deqiang & Zhang, Ruimei & Liu, Yajuan & Zhong, Shouming, 2017. "Sampled-data synchronization of chaotic Lur’e systems via input-delay-dependent-free-matrix zero equality approach," Applied Mathematics and Computation, Elsevier, vol. 315(C), pages 34-46.
    2. Zhang, Shuai & Yang, Yongqing & Sui, Xin & Xu, Xianyu, 2019. "Finite-time synchronization of memristive neural networks with parameter uncertainties via aperiodically intermittent adjustment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    3. Xiao, Jianying & Zhong, Shouming, 2018. "Extended dissipative conditions for memristive neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 145-163.
    4. Omar Guillén-Fernández & Esteban Tlelo-Cuautle & Luis Gerardo de la Fraga & Yuma Sandoval-Ibarra & Jose-Cruz Nuñez-Perez, 2022. "An Image Encryption Scheme Synchronizing Optimized Chaotic Systems Implemented on Raspberry Pis," Mathematics, MDPI, vol. 10(11), pages 1-23, June.
    5. Qi, Xingnan & Bao, Haibo & Cao, Jinde, 2019. "Exponential input-to-state stability of quaternion-valued neural networks with time delay," Applied Mathematics and Computation, Elsevier, vol. 358(C), pages 382-393.
    6. Li, Xiaoqing & She, Kun & Zhong, Shouming & Shi, Kaibo & Kang, Wei & Cheng, Jun & Yu, Yongbin, 2018. "Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 325(C), pages 271-290.
    7. Zhang, Lan & Yang, Xinsong & Xu, Chen & Feng, Jianwen, 2017. "Exponential synchronization of complex-valued complex networks with time-varying delays and stochastic perturbations via time-delayed impulsive control," Applied Mathematics and Computation, Elsevier, vol. 306(C), pages 22-30.
    8. Huang, Chengdai & Cao, Jinde & Xiao, Min & Alsaedi, Ahmed & Hayat, Tasawar, 2017. "Bifurcations in a delayed fractional complex-valued neural network," Applied Mathematics and Computation, Elsevier, vol. 292(C), pages 210-227.
    9. Kwon, W. & Koo, Baeyoung & Lee, S.M., 2018. "Novel Lyapunov–Krasovskii functional with delay-dependent matrix for stability of time-varying delay systems," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 149-157.
    10. Tu, Zhengwen & Zhao, Yongxiang & Ding, Nan & Feng, Yuming & Zhang, Wei, 2019. "Stability analysis of quaternion-valued neural networks with both discrete and distributed delays," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 342-353.
    11. Shi, Kaibo & Wang, Jun & Zhong, Shouming & Zhang, Xiaojun & Liu, Yajuan & Cheng, Jun, 2019. "New reliable nonuniform sampling control for uncertain chaotic neural networks under Markov switching topologies," Applied Mathematics and Computation, Elsevier, vol. 347(C), pages 169-193.
    12. Su, Haipeng & Luo, Runzi & Huang, Meichun & Fu, Jiaojiao, 2022. "Practical fixed time active control scheme for synchronization of a class of chaotic neural systems with external disturbances," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    13. Yacine, Zedjiga & Hamiche, Hamid & Djennoune, Saïd & Mammar, Saïd, 2022. "Finite-time impulsive observers for nonlinear systems represented by Takagi–Sugeno models: Application to a chaotic system," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 192(C), pages 321-352.
    14. Feng, Jianwen & Yang, Pan & Zhao, Yi, 2016. "Cluster synchronization for nonlinearly time-varying delayed coupling complex networks with stochastic perturbation via periodically intermittent pinning control," Applied Mathematics and Computation, Elsevier, vol. 291(C), pages 52-68.
    15. Zhang, Zhi-Ming & He, Yong & Wu, Min & Wang, Qing-Guo, 2017. "Exponential synchronization of chaotic neural networks with time-varying delay via intermittent output feedback approach," Applied Mathematics and Computation, Elsevier, vol. 314(C), pages 121-132.
    16. A.G., Soriano–Sánchez & C., Posadas–Castillo & M.A., Platas–Garza & A., Arellano–Delgado, 2018. "Synchronization and FPGA realization of complex networks with fractional–order Liu chaotic oscillators," Applied Mathematics and Computation, Elsevier, vol. 332(C), pages 250-262.
    17. He, Xinyi & Wang, Yuhan & Li, Xiaodi, 2021. "Uncertain impulsive control for leader-following synchronization of complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    18. Li, Qiaoping & Liu, Sanyang & Chen, Yonggang, 2018. "Combination event-triggered adaptive networked synchronization communication for nonlinear uncertain fractional-order chaotic systems," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 521-535.
    19. Owolabi, Kolade M. & Gómez-Aguilar, J.F. & Karaagac, Berat, 2019. "Modelling, analysis and simulations of some chaotic systems using derivative with Mittag–Leffler kernel," Chaos, Solitons & Fractals, Elsevier, vol. 125(C), pages 54-63.
    20. Ma, Dazhong & Li, Xiaoyu & Sun, Qiuye & Xie, Xiangpeng, 2018. "Fault tolerant synchronization of chaotic systems with time delay based on the double event-triggered sampled control," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 20-31.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:335:y:2018:i:c:p:38-49. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.