Fully Distributed Game Strategy for Second-Order Players and Its Application to Networked Electricity Markets

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-02-05 DOI:10.1109/TSIPN.2025.3538996

Zhenhua Deng;Minghuan Ye;Xiang-Peng Xie;Xiaojun Yang

引用次数: 0

Abstract

In this paper, we study the noncooperative games (NGs) of multi-agent systems. In our problem, the players have private payoff functions, and their decisions are subject to local and coupling nonlinear inequality constraints. Moreover, our problem contains second-order dynamical systems of players. To control these second-order players to autonomously participate in the games, a distributed adaptive strategy is proposed based on state feedback and primal-dual methods. With our method, the updates of the control inputs of all players depend only on their own and neighbors' information, and are independent of global parameters or variables, different from other related methods. By virtue of variational analysis and LaSalle invariance principle, it is proved that our strategy converges to the variational Generalized Nash Equilibrium (v-GNE) of the games. Finally, the proposed method is applied to networked electricity market games of smart grids.

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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.