IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v194y2025ics0960077925002899.html
   My bibliography  Save this article

Robust iterative learning control for Takagi-Sugeno fuzzy nonlinear systems via preview control

Author

Listed:
  • Li, Li
  • Hui, Ye
  • Chen, Jia

Abstract

Preview control utilizes future reference signal information to enhance system dynamic response and tracking performance. Iterative learning control represents an effective approach for managing repetitive tasks and has found widespread applications in industrial systems. This paper presents an innovative methodology for developing fuzzy iterative learning preview control using the Takagi-Sugeno (T-S) fuzzy model is discussed. The design incorporates robustness against time-varying uncertainties, and reference tracking capabilities. To accomplish this objective, the T-S fuzzy system is integrated with time-variant uncertainties to establish an augmented error system, thereby transforming the original fuzzy iterative learning preview control problem issue into a stability analysis of the augmented error systems. Subsequently, two distinct fuzzy iterative learning preview control laws are formulated by incorporating the T-S fuzzy system's states or outputs, tracking error, and a previewed reference signal to address the tracking control challenge. Novel sufficient conditions for the asymptotic stability of the augmented error system are derived using the linear matrix inequality (LMI) technique and fuzzy Lyapunov function analysis. Finally, the effectiveness of both proposed control strategies is validated through two numerical examples.

Suggested Citation

  • Li, Li & Hui, Ye & Chen, Jia, 2025. "Robust iterative learning control for Takagi-Sugeno fuzzy nonlinear systems via preview control," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:chsofr:v:194:y:2025:i:c:s0960077925002899
    DOI: 10.1016/j.chaos.2025.116276
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077925002899
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2025.116276?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. Zhenglei Zhang & Jirong Wang & Junwei Gao & Huabo Liu, 2023. "Robust State Estimation for T–S Fuzzy Markov Jump Systems," Mathematics, MDPI, vol. 11(2), pages 1-14, January.
    2. Jiang Wu & Fucheng Liao & Masayoshi Tomizuka, 2017. "Optimal preview control for a linear continuous-time stochastic control system in finite-time horizon," International Journal of Systems Science, Taylor & Francis Journals, vol. 48(1), pages 129-137, January.
    3. Chen, Zhengquan & Hou, Yandong & Huang, Ruirui & Cheng, Qianshuai, 2024. "Neural network compensator-based robust iterative learning control scheme for mobile robots nonlinear systems with disturbances and uncertain parameters," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    4. Gao, Yuxuan & Zhao, Ying & Liu, Yu & Pan, Zhuo-Rui & Wang, Rui, 2023. "Output tracking anti-disturbance control for turbofan systems," Applied Mathematics and Computation, Elsevier, vol. 456(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. Wang, H.-Z. & Shangguan, X.-C. & Xiong, D. & An, Y.-H. & Jin, L., 2025. "A matrix-separation-based integral inequality for aperiodic sampled-data synchronization of delayed neural networks considering communication delay," Applied Mathematics and Computation, Elsevier, vol. 486(C).
    2. Tang, Li & Liu, Wei & Liu, Yan-Jun, 2024. "Dual design of control law and switching law for turbofan systems under multiple disturbances," Energy, Elsevier, vol. 296(C).
    3. Li Li & Fucheng Liao, 2020. "Preview Control for MIMO Discrete-Time System with Parameter Uncertainty," Mathematics, MDPI, vol. 8(5), pages 1-20, May.
    4. Wang, Bo & Zou, Fucheng & Wu, Junhui & Cheng, Jun, 2025. "A learning-based sliding mode control for switching systems with dead zone," Applied Mathematics and Computation, Elsevier, vol. 494(C).
    5. Chen Jia & Li Li, 2024. "Model-Following Preview Control for a Class of Linear Descriptor Systems with Actuator Failures," Mathematics, MDPI, vol. 12(22), pages 1-16, November.

    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:chsofr:v:194:y:2025:i:c:s0960077925002899. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.