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Centralized and Decentralized Event-Triggered Nash Equilibrium-Seeking Strategies for Heterogeneous Multi-Agent Systems

Author

Listed:
  • Liu He

    (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Hui Cheng

    (School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Yunong Zhang

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China)

Abstract

This paper addresses the event-triggered Nash equilibrium-seeking problem for non-cooperative games played by heterogeneous multi-agent systems. Unlike homogeneous multi-agent systems, heterogeneous multi-agent systems consist of agents with different dynamic structures, making it difficult to design control schemes and construct event-triggering conditions for such systems. In this paper, a novel centralized event-triggered Nash equilibrium-seeking strategy and a novel decentralized event-triggered Nash equilibrium-seeking strategy are proposed. The corresponding centralized and decentralized event-triggering conditions are derived. The convergence properties of the proposed centralized and decentralized strategies are proved. Further theoretical analyses illustrate that Zeno behavior does not exist under the proposed strategies. Finally, the effectiveness and efficiency of both centralized and decentralized strategies are presented through numerical experiments. The experimental results illustrate that under both strategies, heterogeneous multi-agent systems achieve the Nash equilibrium successfully, and the communication consumption among agents is significantly reduced.

Suggested Citation

  • Liu He & Hui Cheng & Yunong Zhang, 2025. "Centralized and Decentralized Event-Triggered Nash Equilibrium-Seeking Strategies for Heterogeneous Multi-Agent Systems," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:419-:d:1578343
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    References listed on IDEAS

    as
    1. Chonglin Jing & Chaoli Wang & Hongkai Song & Yibo Shi & Longyan Hao, 2024. "Optimal Asymptotic Tracking Control for Nonzero-Sum Differential Game Systems with Unknown Drift Dynamics via Integral Reinforcement Learning," Mathematics, MDPI, vol. 12(16), pages 1-21, August.
    2. Hanshan Li & Yun Hao & Xiaoqian Zhang, 2022. "Offensive/Defensive Game Target Damage Assessment Mathematical Calculation Method between the Projectile and Target," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    3. Jia Liu & Cuixia Li, 2023. "Dynamic Game Analysis on Cooperative Advertising Strategy in a Manufacturer-Led Supply Chain with Risk Aversion," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
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