IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v494y2025ics0096300325000104.html
   My bibliography  Save this article

A learning-based sliding mode control for switching systems with dead zone

Author

Listed:
  • Wang, Bo
  • Zou, Fucheng
  • Wu, Junhui
  • Cheng, Jun

Abstract

This paper focuses on the problem of adaptive neural network sliding mode control for switching systems affected by dead zones. Distinct from existing rules defined by transition and sojourn probabilities, a broader switching rule is proposed based on duration-time-dependent sojourn probabilities. A neural network strategy for compensation is implemented to mitigate the effects of the dead zone. Moreover, a sliding mode control law incorporating a learning term is designed, effectively reducing chattering compared to conventional sliding mode control. Employing a stochastic Lyapunov function grounded in the joint distribution of duration time and system mode, sufficient criteria for designing the adaptive neural network-based controller are established. Finally, the effectiveness of the proposed method is demonstrated through two simulated examples.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:apmaco:v:494:y:2025:i:c:s0096300325000104
    DOI: 10.1016/j.amc.2025.129283
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325000104
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2025.129283?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

    for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Wang, Xianjia & Yang, Zhipeng & Chen, Guici & Liu, Yanli, 2024. "Enhancing cooperative evolution in spatial public goods game by particle swarm optimization based on exploration and q-learning," Applied Mathematics and Computation, Elsevier, vol. 469(C).
    3. Minggang Liu & Ning Xu, 2024. "Adaptive neural predefined-time hierarchical sliding mode control of switched under-actuated nonlinear systems subject to bouc-wen hysteresis," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(13), pages 2659-2676, October.
    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. Pan Han-huai & Wang Lin-wei & Liu Hao & MohammadJavad Abdollahi, 2025. "Identifying influential nodes in complex networks: a semi-local centrality measure based on augmented graph and average shortest path theory," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(1), pages 1-18, March.
    2. Wang, Chengjie & Deng, Juan & Zhao, Hui & Li, Li, 2024. "Effect of Q-learning on the evolution of cooperation behavior in collective motion: An improved Vicsek model," Applied Mathematics and Computation, Elsevier, vol. 482(C).
    3. Liu, Minggang & Xu, Ning & Niu, Ben & Alotaibi, Naif D., 2025. "Sliding-mode surface-based fixed-time adaptive critic tracking control for zero-sum game of switched nonlinear systems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 229(C), pages 78-95.
    4. Cai, Wenbo & Chang, Xingzhi & Yang, Ping, 2024. "Link prediction in multilayer social networks using reliable local random walk and boosting ensemble classifier," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    5. 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).
    6. Kang, Hongwei & Li, Xin & Shen, Yong & Sun, Xingping & Chen, Qingyi, 2024. "Particle swarm optimization with historical return decay enhances cooperation in public goods games with investment risks," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
    7. 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).
    8. Ting Lei, 2025. "Securing Fog-enabled IoT: federated learning and generative adversarial networks for intrusion detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(1), pages 1-12, March.
    9. Lin, Jiaying & Long, Pinduo & Liang, Jinfeng & Dai, Qionglin & Li, Haihong & Yang, Junzhong, 2025. "The coevolution of cooperation: Integrating Q-learning and occasional social interactions in evolutionary games," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:494:y:2025:i:c:s0096300325000104. 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.