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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
M. Ye, “On resilience against cyber-physical uncertainties in distributed Nash equilibrium seeking strategies for heterogeneous games,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–10, Jan. 2025.
Citation: M. Ye, “On resilience against cyber-physical uncertainties in distributed Nash equilibrium seeking strategies for heterogeneous games,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 1, pp. 1–10, Jan. 2025.
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On Resilience Against Cyber-Physical Uncertainties in Distributed Nash Equilibrium Seeking Strategies for Heterogeneous Games

Funds:  This work was supported by the National Key R&D Program of China (2022ZD0119604), the National Natural Science Foundation of China (NSFC), (62173181, 62222308, 62221004), and the Natural Science Foundation of Jiangsu Province (BK20220139)
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    Abstract
  • This paper designs distributed Nash equilibrium seeking strategies for heterogeneous dynamic cyber-physical systems. In particular, we are concerned with parametric uncertainties in the control channel of the players. Moreover, the weights on communication links can be compromised by time-varying uncertainties, which can result from possibly malicious attacks, faults and disturbances. To deal with the unavailability of measurement of optimization errors, an output observer is constructed, based on which adaptive laws are designed to compensate for physical uncertainties. With adaptive laws, a new distributed Nash equilibrium seeking strategy is designed by further integrating consensus protocols and gradient search algorithms. Moreover, to further accommodate compromised communication weights resulting from cyber-uncertainties, the coupling strengths of the consensus module are designed to be adaptive. As a byproduct, the coupling strengths are independent of any global information. With theoretical investigations, it is proven that the proposed strategies are resilient to these uncertainties and players’ actions are convergent to the Nash equilibrium. Simulation examples are given to numerically validate the effectiveness of the proposed strategies.

     

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    • 1 This paper states that a system is a linear (integrator-type) system with uncertainties if it is a (an) linear (integrator-type) system when uncertainties are removed. Moreover, the uncertainties may be nonlinear, making the whole system nonlinear.
    References(36)
  • [1]
    M. Ye and G. Hu, “Game design and analysis for price based demand response: An aggregate game approach,” IEEE Trans. Cybern., vol. 47, no. 3, pp. 720–730, 2017. doi: 10.1109/TCYB.2016.2524452
    [2]
    Y. Wan, J. Qin, F. Li, X. Yu, and Y. Kang, “Game theoretic-based distributed charging strategy for PEVs in a smart charging station,” IEEE Trans. Smart Grid, vol. 12, no. 1, pp. 538–547, 2021. doi: 10.1109/TSG.2020.3020466
    [3]
    J. Fink, A. Ribeiro, and V. Kumar, “Robust control for mobility and wireless communication in cyber – Physical systems with application to robot teams,” Proc. IEEE, vol. 100, no. 1, pp. 164–178, 2012. doi: 10.1109/JPROC.2011.2161427
    [4]
    M. Fernando, R. Senanayake, and M. Swany, “CoCo games: Graphical game-theoretic swarm control for communication-aware coverage,” IEEE Robotics and Automation Letters, vol. 7, no. 3, pp. 5966–5973, 2022. doi: 10.1109/LRA.2022.3160968
    [5]
    C. Messeri, G. Masotti, A. M. Zanchettin, and P. Rocco, “Human-robot collaboration: Optimizing stress and productivity based on game theory,” IEEE Robotics and Automation Letters, vol. 6, no. 4, pp. 8061–8068, 2021. doi: 10.1109/LRA.2021.3102309
    [6]
    M. Ye, Q.-L. Han, L. Ding, and S. Xu, “Distributed Nash equilibrium seeking in games with partial decision information: A survey,” Proc. IEEE, vol. 111, no. 2, pp. 140–157, 2023. doi: 10.1109/JPROC.2023.3234687
    [7]
    Y. Tang, “Distributed Nash equilibrium seeking algorithms for uncertain linear multi-agent systems,” in Proc. Asian Control Conf., 2022, pp. 2277–2281.
    [8]
    X. Liu, Y. Zhang, X. Wang, and H. Ji, “Distributed Nash equilibrium seeking design in network of uncertain linear multi-agent systems,” in Proc. IEEE Int. Conf. Control & Automation, 2020, pp. 147–152.
    [9]
    Y. Zhang, S. Liang, X. Wang, and H. Ji, “Distributed Nash equilibrium seeking for aggregative games with nonlinear dynamics under external disturbances,” IEEE Trans. Cybern., vol. 50, no. 12, pp. 4876–4885, 2020. doi: 10.1109/TCYB.2019.2929394
    [10]
    X. Nian, F. Niu, and S. Li, “Nash equilibrium seeking for multicluster games of multiple nonidentical Euler–Lagrange systems,” IEEE Trans. Control of Network Systems, vol. 10, no. 4, pp. 1732–1743, 2023. doi: 10.1109/TCNS.2023.3239547
    [11]
    B. Huang, Z. Meng, and F. Chen, “Distributed nonlinear placement for a class of multicluster Euler–Lagrange systems,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 52, no. 10, pp. 6418–6425, 2022. doi: 10.1109/TSMC.2022.3150071
    [12]
    M. Ye, “Distributed robust seeking of Nash equilibrium for networked games: An extended state observer-based approach,” IEEE Trans. Cybern., vol. 52, no. 3, pp. 1527–1538, 2022. doi: 10.1109/TCYB.2020.2989755
    [13]
    X.-F. Wang, A. R. Teel, X.-M. Sun, K.-Z. Liu, and G. Shao, “A distributed robust two-time-scale switched algorithm for constrained aggregative games,” IEEE Trans. Autom. Control, vol. 68, no. 11, pp. 6525–6540, 2023. doi: 10.1109/TAC.2023.3240981
    [14]
    M. Ye, L. Ding, and J. Yin, “Distributed robust Nash equilibrium seeking for mixed-order games by a neural-network-based approach,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 50, no. 8, pp. 4808–4819, 2023.
    [15]
    Y. Tang, and P. Yi, “Nash equilibrium seeking for high-order multiagent systems with unknown dynamics,” IEEE Trans. Control Network Systems, vol. 10, no. 1, pp. 321–332, 2023. doi: 10.1109/TCNS.2022.3203362
    [16]
    J. Huang, “Distributed Nash equilibrium seeking for a class of uncertain nonlinear systems subject to bounded disturbances,” IEEE Trans. Automatic Control, 2024. DOI 10.1109/TAC.2024.3359525.
    [17]
    Z. Feng, G. Hu, X. Dong, and J. Lv, “Adaptively distributed Nash equilibrium seeking of noncooperative games for uncertain heterogeneous linear multi-agent systems,” IEEE Trans. Network Science and Engineering, vol. 10, no. 6, pp. 3871–3882, 2023.
    [18]
    X. Ge, Q.-L. Han, Q. Wu, X.-M. Zhang, “Resilient and safe platooning control of connected automated vehicles against intermittent Denial-of-Service attacks,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 5, pp. 1234–1251, 2023. doi: 10.1109/JAS.2022.105845
    [19]
    X. Gong, M. V. Basin, Z. Feng, T. Huang, and Y. Cui, “Resilient time-varying formation-tracking of multi-UAV systems against composite attacks: A two-layered framework,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 969–984, 2023. doi: 10.1109/JAS.2023.123339
    [20]
    C. Chen, K. Xie, F. L. Lewis, S. Xie, and R. Fierro, “Adaptive synchronization of multi-agent systems with resilience to communication link faults,” Automatica, vol. 111, p. 108636, 2020. doi: 10.1016/j.automatica.2019.108636
    [21]
    C. Qian and L. Ding, “Fully distributed attack-resilient Nash equilibrium seeking for networked games subject to DoS attacks,” Information Sciences, vol. 641, p. 119080, 2023. doi: 10.1016/j.ins.2023.119080
    [22]
    X.-F. Wang, X.-M. Sun, M. Ye, and K.-Z. Liu, “Robust distributed Nash equilibrium seeking for games under attacks and communication delays,” IEEE Trans. Autom. Control, vol. 67, no. 9, pp. 4892–4899, 2022. doi: 10.1109/TAC.2022.3164984
    [23]
    X. Cai, F. Xiao, and B. Wei, “Resilient Nash equilibrium seeking in multiagent games under false data injection attacks,” IEEE Trans. Systems, Man, and Cybern.: Systems, vol. 53, no. 1, pp. 275–284, 2023. doi: 10.1109/TSMC.2022.3180006
    [24]
    Y. Cheng, L. Zhang, and S. Liu, “Nash equilibrium strategy in stochastic non-cooperative games,” in Proc. IEEE Int. Conf. Control and Automation, 2020, pp. 530–535.
    [25]
    X. Fang, G. Wen, J. Zhou, and W. X. Zheng, “Distributed adaptive Nash equilibrium seeking over multi-agent networks with communication uncertainties,” in Proc. IEEE Conf. Decision and Control, 2021, pp. 3387–3392.
    [26]
    W. Ren and R. W. Beard, Distributed Consensus in Multi-Vehicle Cooperative Control, London UK: Springer London, 2008.
    [27]
    Z. Li, Z. Wu, Z. Li, and Z. Ding, “Distributed optimal coordination for heterogeneous linear multiagent systems with event-triggered mechanisms,” IEEE Trans. Autom. Control, vol. 65, no. 4, pp. 1763–1770, 2020. doi: 10.1109/TAC.2019.2937500
    [28]
    H. Khailil, Nonlinear Systems, Upper Saddle River, USA: Prentice Hall, 2002.
    [29]
    F. Facchinei, and J.-S. Pang, Finite-Dimensional Variational Inequalities and Complementarity Problems, Springer, 2003.
    [30]
    J. Mei, W. Ren, and Y. Song, “A Unified framework for adaptive leaderless consensus of uncertain multiagent systems under directed graphs,” IEEE Trans. Autom. Control, vol. 66, no. 12, pp. 6179–6186, 2021. doi: 10.1109/TAC.2021.3062594
    [31]
    M. Ye and G. Hu, “Adaptive approaches for fully distributed Nash equilibrium seeking in networked games,” Automatica, vol. 129, p. 109661, 2021. doi: 10.1016/j.automatica.2021.109661
    [32]
    M. Ye, Q.-L. Han, L. Ding, and S. Xu, “Fully distributed Nash equilibrium seeking for high-order players with actuator limitations,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1434–1444, 2023. doi: 10.1109/JAS.2022.105983
    [33]
    X. Ge, Q.-L. Han, X.-M. Zhang, and D. Ding, “Communication resource-efficient vehicle platooning control with various spacing policies,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 362–376, 2024. doi: 10.1109/JAS.2023.123507
    [34]
    L. Zou, Z. Wang, B. Shen, H. Dong, and G. Lu, “Encrypted finitehorizon energy-to-peak state estimation for time-varying systems under eavesdropping attacks: tackling secrecy capacity,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 985–996, 2023. doi: 10.1109/JAS.2023.123393
    [35]
    L. Zou, Z. Wang, B. Shen, and H. Dong, “Moving horizon estimation over relay channels: Dealing with packet losses,” Automatica, vol. 155, p. 111079, 2023. doi: 10.1016/j.automatica.2023.111079
    [36]
    L. Zou, Z. Wang, B. Shen, and H. Dong, “Encryption-decryption-based state estimation with multirate measurements against eavesdroppers: A recursive minimumvariance approach,” IEEE Trans. Autom. Control, vol. 68, no. 12, pp. 8111–8118, 2023. doi: 10.1109/TAC.2023.3288624
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