IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v512y2026ics0096300325004916.html

Distributed reinforcement learning-based FTC for nonlinear multi-agent systems with performance guarantees

Author

Listed:
  • Cui, Lili
  • Xie, Xiangpeng
  • Zhang, Yong

Abstract

This paper focuses on the fault-tolerance consensus problem of nonlinear multi-agent systems (MASs) affected by time-varying actuator faults. Initially, the original problem is redefined as an equivalent N-player nonlinear differential graphical game by introducing a novel cost function for each agent. Based on the Nash solutions, a fault-tolerant control (FTC) strategy is developed to guarantee the asymptotic stability of the faulty MASs and maintain the cost function for each agent within an upper bound. Subsequently, a critic-only reinforcement learning (RL) approach is utilized to acquire the Nash solutions, accompanied by the rigorous stability analysis. It is important to highlight that the proposed RL-based FTC strategy relies exclusively on local state information. Finally, simulation results demonstrate the efficiency of the developed distributed RL-based FTC strategy.

Suggested Citation

  • Cui, Lili & Xie, Xiangpeng & Zhang, Yong, 2026. "Distributed reinforcement learning-based FTC for nonlinear multi-agent systems with performance guarantees," Applied Mathematics and Computation, Elsevier, vol. 512(C).
    • Handle: RePEc:eee:apmaco:v:512:y:2026:i:c:s0096300325004916
    DOI: 10.1016/j.amc.2025.129766
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325004916
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2025.129766?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. Liang, Yuling & Zhang, Huaguang & Zhang, Juan & Luo, Yanhong, 2021. "Integral reinforcement learning-based guaranteed cost control for unknown nonlinear systems subject to input constraints and uncertainties," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    2. 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).
    3. Sader, Malika & Chen, Zengqiang & Liu, Zhongxin & Deng, Chao, 2021. "Distributed robust fault-tolerant consensus control for a class of nonlinear multi-agent systems with intermittent communications," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    4. Ma, Dazhong & Wang, Tianbiao & Zhang, Huaguang & Xie, Xiangpeng, 2021. "Adaptive fault-tolerant output regulation of linear systems with unknown dynamics and actuator faults," Applied Mathematics and Computation, Elsevier, vol. 402(C).
    5. Wang, Yun & Fang, Tian & Kong, Qingkai & Li, Feng, 2024. "Zero-sum game-based optimal control for discrete-time Markov jump systems: A parallel off-policy Q-learning method," Applied Mathematics and Computation, Elsevier, vol. 467(C).
    6. Guo, Siyu & Pan, Yingnan & Li, Hongyi, 2025. "Dynamic event-driven optimal consensus control for state-constrained multiagent zero-sum differential graphical games," Applied Mathematics and Computation, Elsevier, vol. 484(C).
    7. Cui, Lili & Xie, Xiangpeng & Guo, Hongyan & Luo, Yanhong, 2022. "Dynamic event-triggered distributed guaranteed cost FTC scheme for nonlinear interconnected systems via ADP approach," Applied Mathematics and Computation, Elsevier, vol. 425(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. Li, Weixun & Du, Xiangyang & Xiao, Jingyu & Zhang, Limin, 2023. "Bipartite hybrid formation tracking control for heterogeneous multi-agent systems in multi-group cooperative-competitive networks," Applied Mathematics and Computation, Elsevier, vol. 456(C).
    2. Yang, Yang & Wang, Xue & Wu, Yingcheng & Yue, Wenbin, 2023. "Dynamic average consensus control based on event-triggered cloud access," Applied Mathematics and Computation, Elsevier, vol. 453(C).
    3. Wang, Xin & Yang, DongSheng & Li, Weihua & Qin, Jia, 2024. "Designing adaptive continuous event-triggered consensus protocol for nonlinear multi-agent systems with a nonautonomous leader," Applied Mathematics and Computation, Elsevier, vol. 479(C).
    4. Liang, Zhanlue & Liu, Xinzhi, 2025. "Hybrid impulsive cooperative control of vehicle platoons with switching communication topology," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    5. Wang, Yun & Fang, Tian & Kong, Qingkai & Li, Feng, 2024. "Zero-sum game-based optimal control for discrete-time Markov jump systems: A parallel off-policy Q-learning method," Applied Mathematics and Computation, Elsevier, vol. 467(C).
    6. Mu, Yunfei & Zhang, Huaguang & Yan, Yuqing & Wang, Yingchun, 2023. "A novel design approach to state and fault estimation for interconnected systems using distributed observer," Applied Mathematics and Computation, Elsevier, vol. 449(C).
    7. Pessim, Paulo S.P. & Coutinho, Pedro Henrique Silva & Lacerda, Márcio J. & Palhares, Reinaldo Martínez, 2023. "Distributed event-triggered fuzzy control for nonlinear interconnected systems," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    8. Arockia Samy, Stephen & Jeong, Jae Hoon & Bai, Xiaoshan & Joo, Young Hoon, 2025. "Reliable memory sampled-data control for leader-following consensus of nonlinear multi-agent systems and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 238(C), pages 361-382.
    9. Xie, Xiangpeng & Shen, Xicheng & Peng, Chen, 2022. "Relaxed stabilization synthesis of discrete-time nonlinear systems with uplink data loss based on a novel online evaluation mechanism," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    10. Zhu, Fanglai & Du, Wenqing, 2024. "Observer-based consensus of multi-agent systems under odd distributed impulsive control protocol," Applied Mathematics and Computation, Elsevier, vol. 466(C).
    11. Mei, Jun & Jian, Hang & Li, Yan & Wang, Weifeng & Lin, Dong, 2024. "Deep neural networks-based output-dependent intermittent control for a class of uncertain nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 185(C).
    12. Dong, Xu & Zhang, Huaguang & Ming, Zhongyang & Luo, Yanhong, 2025. "Optimal finite-horizon tracking control in affine nonlinear systems: A Stackelberg game approach with H2/H∞ framework," Applied Mathematics and Computation, Elsevier, vol. 495(C).
    13. Zhu, Zhibin & Wang, Fuyong & Yin, Yanhui & Liu, Zhongxin & Chen, Zengqiang, 2022. "Distributed fault-tolerant containment control for a class of non-linear multi-agent systems via event-triggered mechanism," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    14. 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).
    15. Wang, Xianming & Shen, Mouquan, 2023. "Model free optimal control of unknown nonaffine nonlinear systems with input quantization and DoS attack," Applied Mathematics and Computation, Elsevier, vol. 448(C).
    16. Mei, Xu & Cheng, Jun & Huang, Wentao, 2025. "Attack-compensated asynchronous output feedback control for stochastic switching systems with sojourn probability," Applied Mathematics and Computation, Elsevier, vol. 485(C).
    17. Zhao, Guangtong & Cao, Liang & Li, Xiaomeng & Zhou, Qi, 2022. "Observer-based dynamic event-triggered control for nonstrict-feedback stochastic nonlinear multiagent systems," Applied Mathematics and Computation, Elsevier, vol. 430(C).
    18. Pan, Ting-Ting & Xiong, Jun, 2026. "Fault-tolerant control of EVs lateral dynamics with deception attack and multi-channel dynamic quantization," Applied Mathematics and Computation, Elsevier, vol. 514(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:512:y:2026:i:c:s0096300325004916. 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.