Distributed Adaptive Nash Equilibrium Solution for Differential Graphical Games

被引:19
|
作者
Qian, Yang-Yang [1 ]
Liu, Mushuang [1 ]
Wan, Yan [1 ]
Lewis, Frank L. [2 ]
Davoudi, Ali [1 ]
机构
[1] Univ Texas Arlington, Dept Elect Engn, Arlington, TX 76019 USA
[2] Univ Texas Arlington, UTA Res Inst, Ft Worth, TX 76118 USA
基金
美国国家科学基金会;
关键词
Nash equilibrium; Games; Synchronization; Performance analysis; Optimal control; Decentralized control; Microgrids; Adaptive control; differential games; distributed control; microgrid control; synchronization; COOPERATIVE OPTIMAL-CONTROL; LINEAR MULTIAGENT SYSTEMS; SECONDARY CONTROL; SYNCHRONIZATION; CONSENSUS; POLICY; ALGORITHM; NETWORKS; FEEDBACK;
D O I
10.1109/TCYB.2021.3114749
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article investigates differential graphical games for linear multiagent systems with a leader on fixed communication graphs. The objective is to make each agent synchronize to the leader and, meanwhile, optimize a performance index, which depends on the control policies of its own and its neighbors. To this end, a distributed adaptive Nash equilibrium solution is proposed for the differential graphical games. This solution, in contrast to the existing ones, is not only Nash but also fully distributed in the sense that each agent only uses local information of its own and its immediate neighbors without using any global information of the communication graph. Moreover, the asymptotic stability and global Nash equilibrium properties are analyzed for the proposed distributed adaptive Nash equilibrium solution. As an illustrative example, the differential graphical game solution is applied to the microgrid secondary control problem to achieve fully distributed voltage synchronization with optimized performance.
引用
收藏
页码:2275 / 2287
页数:13
相关论文
共 50 条
  • [21] Distributed ε-Nash equilibrium seeking in aggregative games with approximation
    Xu, Gehui
    Chen, Guanpu
    Qi, Hongsheng
    Hong, Yiguang
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 1293 - 1298
  • [22] Distributed Nash Equilibrium Seeking of A Class of Aggregative Games
    Liang, Shu
    Yi, Peng
    Hong, Yiguang
    2017 13TH IEEE INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION (ICCA), 2017, : 58 - 63
  • [23] Distributed Nash Equilibrium Seeking for Games With Unknown Nonlinear Players via Fuzzy Adaptive Method
    Chen, Ying
    Ma, Qian
    IEEE Transactions on Fuzzy Systems, 2024, 32 (11) : 6449 - 6459
  • [24] NASH-EQUILIBRIUM IN STOCHASTIC DIFFERENTIAL-GAMES
    GAIDOV, SD
    COMPUTERS & MATHEMATICS WITH APPLICATIONS-PART A, 1986, 12 (06): : 761 - 768
  • [25] NASH EQUILIBRIUM PAYOFFS FOR STOCHASTIC DIFFERENTIAL GAMES WITH REFLECTION
    Lin, Qian
    ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS, 2013, 19 (04) : 1189 - 1208
  • [26] Deep Reinforcement Learning for Nash Equilibrium of Differential Games
    Li, Zhenyu
    Luo, Yazhong
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, : 1 - 15
  • [27] Multi-Agent Discrete-Time Graphical Games: Interactive Nash Equilibrium and Value Iteration Solution
    Abouheaf, Mohammed
    Lewis, Frank
    Haesaert, Sofie
    Babuska, Robert
    Vamvoudakis, Kyriakos
    2013 AMERICAN CONTROL CONFERENCE (ACC), 2013, : 4189 - 4195
  • [28] Distributed gradient methods to reach a Nash equilibrium in potential games
    Varma, Vineeth S.
    Veetaseveera, Jomphop
    Postoyan, Romain
    Morarescu, Irinel-Constantin
    2021 60TH IEEE CONFERENCE ON DECISION AND CONTROL (CDC), 2021, : 3098 - 3103
  • [29] Distributed Algorithms for Searching Generalized Nash Equilibrium of Noncooperative Games
    Lu, Kaihong
    Jing, Gangshan
    Wang, Long
    IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (06) : 2362 - 2371
  • [30] Distributed Nash Equilibrium Seeking for Aggregative Games With Quantization Constraints
    Pei, Yingqing
    Tao, Ye
    Gu, Haibo
    Lu, Jinhu
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS, 2023, 70 (06) : 2537 - 2549