IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v434y2022ics0096300322005148.html
   My bibliography  Save this article

Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems

Author

Listed:
  • Hua, Yu
  • Zhang, Tianping
  • Xia, Xiaonan

Abstract

In this article, an event-triggered adaptive neural command-filter-based dynamic surface control (CFDSC) is discussed for states constrained nonstrict-feedback nonlinear systems with dynamic uncertainties. The order-reduced compensation signals are designed to compensate the tracking error of the filter in original dynamic surface control (DSC). The hyperbolic tangent function is adopted as the invertible mapping to deal with the full state constraints. An auxiliary signal is employed to estimate the dynamical uncertainties produced by the unmodeled dynamics. The unknown smooth nonlinear terms are approximated by radial basis function neural networks (RBFNNs) at recursive each step. An event-triggered input is constructed in this novel CFDSC framework. With the help of the defined compact set in the stability analysis, the semi-global uniform ultimate boundedness (SGUUB) of all the signals in the adaptive closed-loop system is proved. Moreover, each state can be strictly limited within the time-varying state conditions. Two simulation verifications are employed to verify and clarify the theoretical findings.

Suggested Citation

  • Hua, Yu & Zhang, Tianping & Xia, Xiaonan, 2022. "Event-triggered adaptive neural command-filter-based dynamic surface control for state constrained nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 434(C).
    • Handle: RePEc:eee:apmaco:v:434:y:2022:i:c:s0096300322005148
    DOI: 10.1016/j.amc.2022.127440
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300322005148
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2022.127440?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. Wu, Jing & Sun, Wei & Su, Shun-Feng & Xia, Jianwei, 2022. "Neural-based adaptive control for nonlinear systems with quantized input and the output constraint," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    2. Qing-Yuan Xu & Xiao-Dong Li, 2018. "Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(9), pages 1878-1894, July.
    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. 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).
    2. Fu, Yingying & Li, Jing & Li, Xiaobo & Wu, Shuiyan, 2023. "Dynamic event-triggered adaptive control for uncertain stochastic nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    3. Shuxian Lun & Zhaoyi Lv & Xiaodong Lu & Ming Li, 2023. "ESN-Observer-Based Adaptive Stabilization Control for Delayed Nonlinear Systems with Unknown Control Gain," Mathematics, MDPI, vol. 11(13), pages 1-21, July.
    4. Truong, Hoai Vu Anh & Phan, Van Du & Tran, Duc Thien & Ahn, Kyoung Kwan, 2024. "A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 475(C).
    5. Yun Ho Choi & Sung Jin Yoo, 2020. "Tracking Control Strategy Using Filter-Based Approximation for the Unknown Control Direction Problem of Uncertain Pure-Feedback Nonlinear Systems," Mathematics, MDPI, vol. 8(8), pages 1-17, August.
    6. Liu, Yanli & Hao, Li-Ying, 2024. "Adaptive tracking control for constrained nonlinear nonstrict-feedback switched stochastic systems with unknown control directions," Applied Mathematics and Computation, Elsevier, vol. 473(C).
    7. Liu, Yanli & Wang, Runzhi & Hao, Li-Ying, 2022. "Adaptive TD control of full-state-constrained nonlinear stochastic switched systems," Applied Mathematics and Computation, Elsevier, vol. 427(C).
    8. Zhang, Yanqi & Wang, Zhenlei & Wang, Xin, 2023. "Adaptive modified prescribed performance constraint control for uncertain nonlinear discrete-time systems," Applied Mathematics and Computation, Elsevier, vol. 441(C).
    9. Yun-Shan Wei & Qing-Yuan Xu, 2018. "Iterative Learning Control for Linear Discrete-Time Systems with Randomly Variable Input Trail Length," Complexity, Hindawi, vol. 2018, pages 1-6, November.
    10. Cui, Di & Zou, Wencheng & Guo, Jian & Xiang, Zhengrong, 2022. "Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    11. Li, Jiahao & Liu, Yu & Yu, Jinyong, 2022. "A new result on semi-synchronous event-triggered backstepping robust control for a class of non-Lipschitzian networked systems," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    12. Hu, Yifan & Liu, Wenhui, 2023. "Adaptive fuzzy dynamic surface control for nonstrict-feedback nonlinear state constrained systems with input dead-zone via event-triggered sampling," Applied Mathematics and Computation, Elsevier, vol. 450(C).
    13. Qing-Yuan Xu & Wan-Ying He & Chuang-Tao Zheng & Peng Xu & Yun-Shan Wei & Kai Wan, 2022. "Adaptive Fuzzy Iterative Learning Control for Systems with Saturated Inputs and Unknown Control Directions," Mathematics, MDPI, vol. 10(19), pages 1-17, September.
    14. Wang, Sanxia & Xia, Jianwei & Wang, Xueliang & Yang, Wenjing & Wang, Linqi, 2021. "Adaptive neural networks control for MIMO nonlinear systems with unmeasured states and unmodeled dynamics," Applied Mathematics and Computation, Elsevier, vol. 408(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:eee:apmaco:v:434:y:2022:i:c:s0096300322005148. 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.