IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i10p2257-d1144816.html
   My bibliography  Save this article

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Author

Listed:
  • Saravanan Shanmugam

    (Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamilnadu, India)

  • Rajarathinam Vadivel

    (Department of Mathematics, Faculty of Science and Technology, Phuket Rajabhat University, Phuket 83000, Thailand)

  • Nallappan Gunasekaran

    (Computational Intelligence Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan)

Abstract

In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

Suggested Citation

  • Saravanan Shanmugam & Rajarathinam Vadivel & Nallappan Gunasekaran, 2023. "Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation," Mathematics, MDPI, vol. 11(10), pages 1-24, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2257-:d:1144816
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/10/2257/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/10/2257/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Tao & Cao, Jinde & Xiong, Lianglin & Zhang, Haiyang & Shu, Jinlong, 2022. "Sampled-data synchronization criteria for Markovian jumping neural networks with additive time-varying delays using new techniques," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    2. Zhang, Mengjiao & Zang, Hongyan & Bai, Luyuan, 2022. "A new predefined-time sliding mode control scheme for synchronizing chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. Zhang, Dian & Cheng, Jun & Cao, Jinde & Zhang, Dan, 2019. "Finite-time synchronization control for semi-Markov jump neural networks with mode-dependent stochastic parametric uncertainties," Applied Mathematics and Computation, Elsevier, vol. 344, pages 230-242.
    4. Chang, Xiao-Heng & Jin, Xue, 2022. "Observer-based fuzzy feedback control for nonlinear systems subject to transmission signal quantization," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    5. Song, Xingxing & Lu, Hongqian & Xu, Yao & Zhou, Wuneng, 2022. "H∞ synchronization of semi-Markovian jump neural networks with random sensor nonlinearities via adaptive event-triggered output feedback control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 198(C), pages 1-19.
    6. Shuailei Zhang & Xinge Liu & Xuemei Li, 2022. "Finite-time synchronisation of delayed fractional-order coupled neural networks," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(12), pages 2597-2611, September.
    7. Zou, Cong & Li, Bing & Liu, Feiyang & Xu, Bingrui, 2022. "Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays," Applied Mathematics and Computation, Elsevier, vol. 425(C).
    8. Vadivel, R. & Hammachukiattikul, P. & Gunasekaran, Nallappan & Saravanakumar, R. & Dutta, Hemen, 2021. "Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
    Full references (including those not matched with items on IDEAS)

    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. Zhu, Lin & Che, Wei-Wei & Jin, Xiao-Zheng, 2022. "Dynamic event-triggered tracking control for model-free networked control systems," Applied Mathematics and Computation, Elsevier, vol. 431(C).
    2. Li, Xin & Cheng, Kaijie & Zhu, Liangkuan & Wei, Guoliang, 2023. "Outlier-resistant interval observer design for multirate time-delayed systems under the adaptive event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    3. Chen, Xiang & Li, Shi & Wang, Ronghao & Xiang, Zhengrong, 2023. "Event-Triggered output feedback adaptive control for nonlinear switched interconnected systems with unknown control coefficients," Applied Mathematics and Computation, Elsevier, vol. 445(C).
    4. Yan, Zhilian & Guo, Tong & Zhao, Anqi & Kong, Qingkai & Zhou, Jianping, 2022. "Reliable exponential H∞ filtering for a class of switched reaction-diffusion neural networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    5. Zhang, Xiao-Li & Li, Hong-Li & Kao, Yonggui & Zhang, Long & Jiang, Haijun, 2022. "Global Mittag-Leffler synchronization of discrete-time fractional-order neural networks with time delays," Applied Mathematics and Computation, Elsevier, vol. 433(C).
    6. Sun, Meng & Zhuang, Guangming & Xia, Jianwei & Wang, Yanqian & Chen, Guoliang, 2022. "Stochastic admissibility and H∞ output feedback control for singular Markov jump systems under dynamic measurement output event-triggered strategy," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Sakthivel, Ramalingam & Sakthivel, Rathinasamy & Kwon, Oh-Min & Selvaraj, Palanisamy, 2021. "Disturbance rejection for singular semi-Markov jump neural networks with input saturation," Applied Mathematics and Computation, Elsevier, vol. 407(C).
    8. Yang, Wei & Cui, Guozeng & Ma, Qian & Ma, Jiali & Tao, Chongben, 2022. "Finite-time adaptive event-triggered command filtered backstepping control for a QUAV," Applied Mathematics and Computation, Elsevier, vol. 423(C).
    9. Liu, Haoliang & Zhang, Taixiang & Li, Xiaodi, 2021. "Event-triggered control for nonlinear systems with impulse effects," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    10. Zhimin Li & Chengming Lu & Hongyu Wang, 2023. "Non-Fragile Fuzzy Tracking Control for Nonlinear Networked Systems with Dynamic Quantization and Randomly Occurring Gain Variations," Mathematics, MDPI, vol. 11(5), pages 1-20, February.
    11. Mathiyalagan, K. & Nidhi, A. Shree & Su, H. & Renugadevi, T., 2022. "Observer and boundary output feedback control for coupled ODE-transport PDE," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    12. Jiao, Ticao & Qi, Xiaomei & Jiang, Jishun & Yu, Mingzheng, 2022. "Noise-input-to-state stability analysis of switching stochastic nonlinear systems with mode-dependent multiple impulses," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    13. Harshavarthini, S. & Sakthivel, R. & Ma, Yong-Ki & Muslim, M., 2020. "Finite-time resilient fault-tolerant investment policy scheme for chaotic nonlinear finance system," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    14. Harshavarthini, S. & Kwon, O.M. & Lee, S.M., 2022. "Uncertainty and disturbance estimator-based resilient tracking control design for fuzzy semi-Markovian jump systems," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    15. Guo, Beibei & Xiao, Yu, 2023. "Intermittent synchronization for multi-link and multi-delayed large-scale systems with semi-Markov jump and its application of Chua’s circuits," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    16. Luo, Mei & Wang, JinRong & Meng, Deyuan, 2023. "Stochastic convergence problems on switching networks: An event-triggered method," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    17. Chang, Wenting & Sang, Hong & Guo, Liangdong & Wu, Libing & Dimirovski, Georgi M., 2024. "Integrated L∞ anti-disturbance synchronization control for switched neural networks with unknown delays," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    18. Hu, Yifan & Liu, Wenhui & Liu, Guobao, 2022. "Adaptive finite‐time event‐triggered control for uncertain nonlinearly parameterized systems with unknown control direction and actuator failures," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    19. Md Sayeed Anwar & Dibakar Ghosh & Nikita Frolov, 2021. "Relay Synchronization in a Weighted Triplex Network," Mathematics, MDPI, vol. 9(17), pages 1-10, September.
    20. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).

    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:gam:jmathe:v:11:y:2023:i:10:p:2257-:d:1144816. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.