Distributed Nash Equilibrium Seeking for Nonlinear Players With Input Delay

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2024-08-29 DOI:10.1109/TSIPN.2024.3451979

Zhaoming Sheng;Qian Ma

引用次数: 0

Abstract

This paper studies the distributed Nash equilibrium seeking problem for players subject to unknown nonlinear dynamics and input delay. By designing a distributed estimator for each player to estimate other players' decisions and embedding an auxiliary variable to compensate for the influence of unknown nonlinearities, the distributed Nash equilibrium seeking algorithms are obtained for first-, second-, and high-order nonlinear players, respectively. With the help of the Lyapunov stability theory and Lyapunov-Krasovskii functional approach, the maximum allowable input delay is determined and the global asymptotic convergence of players' decisions to the Nash equilibrium is proved. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed methods.

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有输入延迟的非线性参与者的分布式纳什均衡寻求

本文研究了受未知非线性动态和输入延迟影响的棋手的分布式纳什均衡寻求问题。通过为每个棋手设计一个分布式估计器来估计其他棋手的决策，并嵌入一个辅助变量来补偿未知非线性的影响，分别得到了一阶、二阶和高阶非线性棋手的分布式纳什均衡寻求算法。借助李雅普诺夫稳定性理论和李雅普诺夫-克拉索夫斯基函数方法，确定了最大允许输入延迟，并证明了博弈者决策对纳什均衡的全局渐近收敛性。最后，还提供了模拟实例来证明所提方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.