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Adaptive fuzzy ILC of nonlinear discrete-time systems with unknown dead zones and control directions

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  • Qing-Yuan Xu
  • Xiao-Dong Li

Abstract

This paper presents an adaptive fuzzy iterative learning control (ILC) design for non-parametrized nonlinear discrete-time systems with unknown input dead zones and control directions. In the proposed adaptive fuzzy ILC algorithm, a fuzzy logic system (FLS) is used to approximate the desired control signal, and an additional adaptive mechanism is designed to compensate for the unknown input dead zone. In dealing with the unknown control direction of the nonlinear discrete-time system, a discrete Nussbaum gain technique is exploited along the iteration axis and applied to the adaptive fuzzy ILC algorithm. As a result, it is proved that the proposed adaptive fuzzy ILC scheme can drive the ILC tracking errors beyond the initial time instants into a tunable residual set as iteration number goes to infinity, and keep all the system signals bounded in the adaptive ILC process. Finally, a simulation example is used to demonstrate the feasibility and effectiveness of the adaptive fuzzy ILC scheme.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:9:p:1878-1894
    DOI: 10.1080/00207721.2018.1479462
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    Cited by:

    1. 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.
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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).
    8. 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).
    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.

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