IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v475y2024ics0096300324001711.html
   My bibliography  Save this article

A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems

Author

Listed:
  • Truong, Hoai Vu Anh
  • Phan, Van Du
  • Tran, Duc Thien
  • Ahn, Kyoung Kwan

Abstract

Due to the difficulty encountered in dealing with unstructured system dynamics with unmeasured system state variables, this paper presents a novel observer-based neural network finite-time output control strategy for general high-order nonlinear systems (HNSs). The suggested technique is performed based on the backstepping-like control (BSC) scheme with a hybrid nonlinear disturbance-state observer and norm estimation-based radial basis function neural network (RBFNN). This helps not only reduce the number of estimated parameters but also relax the restriction of using inequality when exploiting the norm estimation concept in a conventional way; thus, retaining the same properties of the original system. Therefore, an observer-based finite-time output feedback control is established to deal with the unstructured dynamical behaviors and satisfying the output tracking regulation with the semi-global practically finite-time stability (SGPFS) guaranteed for the closed-loop system. The effectiveness and workability of the proposed algorithm is verified by a numerical simulation on a specific practical application.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:475:y:2024:i:c:s0096300324001711
    DOI: 10.1016/j.amc.2024.128699
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300324001711
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2024.128699?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, Dawei & Sun, Yonghui & Xia, Rongsheng & Lu, Shumin, 2022. "Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    2. 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).
    3. Wu, Ziwen & Zhang, Tianping & Xia, Xiaonan & Hua, Yu, 2022. "Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    4. Wang, Yingchun & Zhang, Jiaxin & Zhang, Huaguang & Xie, Xiangpeng, 2021. "Finite-time adaptive neural control for nonstrict-feedback stochastic nonlinear systems with input delay and output constraints," Applied Mathematics and Computation, Elsevier, vol. 393(C).
    5. 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. Ma, Haoxiang & Xiong, Shixun & Fu, Zhumu & Tao, Fazhan & Ji, Baofeng, 2024. "High-order disturbance observer-based safe tracking control for a class of uncertain MIMO nonlinear systems with time-varying full state constraints," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    2. 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).
    3. Xiongfeng Deng & Xiyu Zhang, 2022. "Adaptive Fuzzy Tracking Control of Uncertain Nonlinear Multi-Agent Systems with Unknown Control Directions and a Dead-Zone Fault," Mathematics, MDPI, vol. 10(15), pages 1-19, July.
    4. Jia, Jinping & Dai, Hao & Zhang, Fandi & Huang, Jianwen, 2022. "Global stabilization of low-order stochastic nonlinear systems with multiple time-varying delays by a continuous feedback control," Applied Mathematics and Computation, Elsevier, vol. 429(C).
    5. Yi, Jiale, 2023. "Adaptive fuzzy connectivity-preserving consensus protocols for stochastic strict-feedback nonlinear MASs subject to unmeasured periodic disturbances," Applied Mathematics and Computation, Elsevier, vol. 444(C).
    6. 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.
    7. 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.
    8. Wang, Boyu & Zhang, Yijun & Wei, Miao, 2023. "Fixed-time leader-following consensus of multi-agent systems with intermittent control," Applied Mathematics and Computation, Elsevier, vol. 438(C).
    9. Yuan, Manman & Zhai, Junyong & Ye, Hui, 2022. "Adaptive output feedback control for a class of switched stochastic nonlinear systems via an event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 422(C).
    10. 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).
    11. 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).
    12. 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.
    13. 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).
    14. 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).
    15. 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.
    16. 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).
    17. Xiongfeng Deng & Jiakai Wang, 2022. "Fuzzy-Based Adaptive Dynamic Surface Control for a Type of Uncertain Nonlinear System with Unknown Actuator Faults," Mathematics, MDPI, vol. 10(10), pages 1-21, May.

    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:475:y:2024:i:c:s0096300324001711. 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.