2.845

2023影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

多智能体协同研究进展综述: 博弈和控制交叉视角

秦家虎 马麒超 李曼 张聪 付维明 刘轻尘 郑卫新

秦家虎, 马麒超, 李曼, 张聪, 付维明, 刘轻尘, 郑卫新. 多智能体协同研究进展综述: 博弈和控制交叉视角. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240508
引用本文: 秦家虎, 马麒超, 李曼, 张聪, 付维明, 刘轻尘, 郑卫新. 多智能体协同研究进展综述: 博弈和控制交叉视角. 自动化学报, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240508
Qin Jia-Hu, Ma Qi-Chao, Li Man, Zhang Cong, Fu Wei-Ming, Liu Qing-Chen, Zheng Wei-Xin. Recent advances on multi-agent collaboration: A cross-perspective of game and control theory. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240508
Citation: Qin Jia-Hu, Ma Qi-Chao, Li Man, Zhang Cong, Fu Wei-Ming, Liu Qing-Chen, Zheng Wei-Xin. Recent advances on multi-agent collaboration: A cross-perspective of game and control theory. Acta Automatica Sinica, xxxx, xx(x): x−xx doi: 10.16383/j.aas.c240508

多智能体协同研究进展综述: 博弈和控制交叉视角

doi: 10.16383/j.aas.c240508 cstr: 32138.14.j.aas.c240508
基金项目: 国家自然科学基金 (U23A20323, 62373341, 62203418, 62303435, 62403444)资助
详细信息
    作者简介:

    秦家虎:中国科学技术大学自动化系教授.主要研究方向为网络化控制系统, 自主智能系统, 以及人-机交互. E-mail: jhqin@ustc.edu.cn

    马麒超:中国科学技术大学自动化系副研究员. 主要研究方向为多智能体系统协同决策与控制, 及其在机器人系统中的应用. E-mail: qcma@ustc.edu.cn

    李曼:中国科学技术大学自动化系副研究员. 主要研究方向为多智能体博弈, 强化学习, 以及人-机交互. E-mail: man.li@ustc.edu.cn

    张聪:中国科学技术大学自动化系博士后.主要研究方向为多智能体协同, 分布式状态估计, 移动机器人同步定位与建图. E-mail: cong_zhang@ustc.edu.cn

    付维明:中国科学技术大学自动化系副研究员. 主要研究方向为多智能体系统协同与智能电网能量管理. E-mail: fwm1993@ustc.edu.cn

    刘轻尘:中国科学技术大学自动化系教授.主要研究方向为网络化系统, 多机器人系统, 以及基于学习的控制. E-mail: qingchen_liu@ustc.edu.cn

    郑卫新:澳大利亚西悉尼大学杰出教授, IEEE Fellow. 主要研究方向为系统辨识, 网络化控制, 多智能体系统, 神经网络, 信号处理. E-mail: w.zheng@westernsydney.edu.au

Recent Advances on Multi-Agent Collaboration: A Cross-Perspective of Game and Control Theory

Funds: Supported in part by National Natural Science Foundation of China (U23A20323, 62373341, 62203418, 62303435, 62403444)
More Information
    Author Bio:

    QIN Jia-Hu Professor at Department of Automation, University of Science and Technology of China. His research interests include networked control systems, autonomous intelligent systems, and human–robot interaction

    MA Qi-Chao Research Associate Professor at Department of Automation, University of Science and Technology of China. His research interests include decision and control of multi-agent systems, with applications to robotics

    LI Man Research Associate Professor at Department of Automation, University of Science and Technology of China. Her research interests include multi-agent games, reinforcement learning, and human–robot interaction

    CONG Zhang Post-Doctoral Researcher at Department of Automation, University of Science and Technology of China. Her research interests include multi-agent cooperation, distributed state estimation, and SLAM

    FU Wei-Ming Professor at Department of Automation, University of Science and Technology of China. His research interests include consensus in multi-agent systems and energy management in smart grids

    LIU Qing-Chen Professor at Department of Automation, University of Science and Technology of China. His research interests include networked systems, multi-robotics system and learning based control

    ZHENG Wei-Xing Distinguished professor at Western Sydney University, Australia. IEEE Fellow. His research interest covers system identification, networked control systems, multi-agent systems, neural networks, and signal processing

  • 摘要: 多智能体协同应用广泛, 并被列为新一代人工智能基础理论亟待突破的重要内容之一, 对其开展研究具有鲜明的科学价值和工程意义.随着人工智能技术的进步, 单一控制视角下的多智能体协同已无法满足执行大规模复杂任务的需求, 融合博弈与控制的多智能体协同应运而生.在这一框架下, 多智能体协同具有更高的灵活性、适应性和扩展性, 为多智能体系统的发展带来了更多可能性. 本文首先从协同角度入手, 回顾了多智能体协同控制与估计领域的进展. 接着, 围绕博弈与控制的融合, 介绍了博弈框架的基本概念, 重点讨论了在微分博弈下多智能体协同问题的建模与分析, 并简要总结了如何应用强化学习算法求解博弈均衡. 文章选取多移动机器人导航和电动汽车充电调度这两个典型的多智能体协同场景, 介绍了博弈与控制融合的思想如何用于解决相关领域的难点问题. 最后, 对博弈与控制融合框架下的多智能体协同进行了总结和展望.
    1)  11 这里的交互指的是信息流动, 例如智能体通过通信或者传感装置获取其他个体的信息2 除非特别声明, 下文均以这里的连续型动力学系统为讨论对象.
    2)  23 如果矩阵$ A $的特征值实部小于等于零, 且实部为零的特征值代数重数等于几何重数, 就称$ A $是边缘稳定的.4 如果矩阵$ A $的特征值实部均为零, 且代数重数等于几何重数, 就称$ A $是中立型稳定的.
    3)  35有界输入下渐近零可控的定义见文献[42]
    4)  46 除非特别说明, 本小节所述时间$ t\in[t_k^i,\;t_{k+1}^i) $.
  • 图  1  论文总体结构

    Fig.  1  General Structure of the Paper

    图  2  第2章总体结构

    Fig.  2  General Structure of Chapter 2

  • [1] 杨涛, 杨博, 殷允强, 虞文武, 夏元清, 洪奕光. 多智能体系统协同控制与优化专刊序言. 控制与决策, 2023, 38(5): 1153−1158

    Yang T, Yang B, Yin Y, Yu W, Xia Y, Hong Y. Guest editorial of special issue on cooperative control and optimization for multi-agent systems. Control and Decision, 2023, 38(5): 1153−1158
    [2] Moreau L. Stability of multiagent systems with time-dependent communication links. IEEE Transactions on Automatic Control, 2005, 50(2): 169−182 doi: 10.1109/TAC.2004.841888
    [3] Cao M, Morse A S, Anderson B D O. Reaching a consensus in a dynamically changing environment: convergence rates, measurement delays, and asynchronous events. SIAM Journal on Control and Optimization, 2008, 47(2): 601−623 doi: 10.1137/060657029
    [4] Shi G, Johansson K H. The role of persistent graphs in the agreement seeking of social networks. IEEE Journal on Selected Areas in Communications, 2013, 31(9): 595−606 doi: 10.1109/JSAC.2013.SUP.0513052
    [5] Qin J, Gao H. A sufficient condition for convergence of sampled-data consensus for double-integrator dynamics with nonuniform and time-varying communication delays. IEEE Transactions on Automatic Control, 2012, 57(9): 2417−2422 doi: 10.1109/TAC.2012.2188425
    [6] Qin J, Zheng W X, Gao H. Consensus of multiple second-order vehicles with a time-varying reference signal under directed topology. Automatica, 2011, 47(9): 1983−1991 doi: 10.1016/j.automatica.2011.05.014
    [7] Qin J, Gao H, Zheng W X. Exponential synchronization of complex networks of linear systems and nonlinear oscillators: A unified analysis. IEEE Transactions on Neural Networks and Learning Systems, 2014, 26(3): 510−521
    [8] Lin Z, Lin Z. Low gain feedback. London: Springer, 1999
    [9] Qin J, Fu W, Zheng W X, et al. On the bipartite consensus for generic linear multiagent systems with input saturation. IEEE Transactions on Cybernetics, 2016, 47(8): 1948−1958
    [10] Meskin N, Khorasani K. Actuator fault detection and isolation for a network of unmanned vehicles. IEEE Transactions on Automatic Control, 2009, 54(4): 835−840 doi: 10.1109/TAC.2008.2009675
    [11] Dimarogonas D V, Frazzoli E, Johansson K H. Distributed event-triggered control for multi-agent systems. IEEE Transactions on Automatic Control, 2011, 57(5): 1291−1297
    [12] Qin J, Ma Q, Shi Y, et al. Recent advances in consensus of multi-agent systems: A brief survey. IEEE Transactions on Industrial Electronics, 2016, 64(6): 4972−4983
    [13] Qin J, Yu C, Gao H. Coordination for linear multiagent systems with dynamic interaction topology in the leader-following framework. IEEE Transactions on Industrial Electronics, 2013, 61(5): 2412−2422
    [14] Zhang J F. Preface to special topic on games in control systems. National Science Review, 2020, 7(7): 1115−1115 doi: 10.1093/nsr/nwaa118
    [15] Shamma J S. Game theory, learning, and control systems. National Science Review, 2020, 7(7): 1118−1119 doi: 10.1093/nsr/nwz163
    [16] 王龙, 黄锋. 多智能体博弈、学习与控制. 自动化学报, 2023, 49(3): 580−613

    Wang L, Huang F. An interdisciplinary survey of multi-agent games, learning, and control. Acta Automatica Sinica, 2023, 49(3): 580−613
    [17] Marden J R, Shamma J S. Game theory and control. Annual Review of Control, Robotics, and Autonomous Systems, 2018, 1: 105−134 doi: 10.1146/annurev-control-060117-105102
    [18] Riehl J, Ramazi P, Cao M. A survey on the analysis and control of evolutionary matrix games. Annual Reviews in Control, 2018, 45: 87−106 doi: 10.1016/j.arcontrol.2018.04.010
    [19] Zhang R R, Guo L. Controllability of Nash equilibrium in game-based control systems. IEEE Transactions on Automatic Control, 2019, 64(10): 4180−4187 doi: 10.1109/TAC.2019.2893150
    [20] Ye M, Hu G. Adaptive approaches for fully distributed Nash equilibrium seeking in networked games. Automatica, 2021, 129: 109661 doi: 10.1016/j.automatica.2021.109661
    [21] Oh K K, Park M C, Ahn H S. A survey of multi-agent formation control. Automatica, 2015, 53 : 424−440
    [22] Zhang Y, Li S. Distributed biased min-consensus with applications to shortest path planning. IEEE Transactions on Automatic Control, 2017, 62(10): 5429−5436 doi: 10.1109/TAC.2017.2694547
    [23] Anderson B D O, Shi G, Trumpf J. Convergence and state reconstruction of time-varying multi-agent systems from complete observability theory. IEEE Transactions on Automatic Control, 2016, 62(5): 2519−2523
    [24] Xiao F, Wang L. Asynchronous consensus in continuous-time multi-agent systems with switching topology and time-varying delays. IEEE Transactions on Automatic Control, 2008, 53(8): 1804−1816 doi: 10.1109/TAC.2008.929381
    [25] Qin J, Gao H, Yu C. On discrete-time convergence for general linear multi-agent systems under dynamic topology. IEEE Transactions on Automatic Control, 2013, 59(4): 1054−1059
    [26] Yang T, Meng Z, Shi G, et al. Network synchronization with nonlinear dynamics and switching interactions. IEEE Transactions on Automatic Control, 2015, 61(10): 3103−3108
    [27] Lu M, Liu L. Distributed feedforward approach to cooperative output regulation subject to communication delays and switching networks. IEEE Transactions on Automatic Control, 2016, 62(4): 1999−2005
    [28] Meng H, Chen Z, Middleton R. Consensus of multiagents in switching networks using input-to-state stability of switched systems. IEEE Transactions on Automatic Control, 2018, 63(11): 3964−3971 doi: 10.1109/TAC.2018.2809454
    [29] Liu T, Huang J. Leader-following attitude consensus of multiple rigid body systems subject to jointly connected switching networks. Automatica, 2018, 92: 63−71 doi: 10.1016/j.automatica.2018.02.012
    [30] Meng Z, Yang T, Li G, et al. Synchronization of coupled dynamical systems: Tolerance to weak connectivity and arbitrarily bounded time-varying delays. IEEE Transactions on Automatic Control, 2017, 63(6): 1791−1797
    [31] Abdessameud A. Consensus of nonidentical Euler–Lagrange systems under switching directed graphs. IEEE Transactions on Automatic Control, 2018, 64(5): 2108−2114
    [32] Kim H, Shim H, Back J, et al. Consensus of output-coupled linear multi-agent systems under fast switching network: Averaging approach. Automatica, 2013, 49(1): 267−272 doi: 10.1016/j.automatica.2012.09.025
    [33] Back J, Kim J S. Output feedback practical coordinated tracking of uncertain heterogeneous multi-agent systems under switching network topology. IEEE Transactions on Automatic Control, 2017, 62(12): 6399−6406 doi: 10.1109/TAC.2017.2651166
    [34] Valcher M E, Zorzan I. On the consensus of homogeneous multi-agent systems with arbitrarily switching topology. Automatica, 2017, 84: 79−85 doi: 10.1016/j.automatica.2017.07.011
    [35] Su Y, Huang J. Stability of a class of linear switching systems with applications to two consensus problems. IEEE Transactions on Automatic Control, 2011, 57(6): 1420−1430
    [36] Wang X, Zhu J, Feng J. A new characteristic of switching topology and synchronization of linear multiagent systems. IEEE Transactions on Automatic Control, 2018, 64(7): 2697−2711
    [37] Ma Q, Qin J, Zheng W X, et al. Exponential consensus of linear systems over switching network: A subspace method to establish necessity and sufficiency. IEEE Transactions on Cybernetics, 2020, 52(3): 1565−1574
    [38] Ma Q, Qin J, Yu X, et al. On necessary and sufficient conditions for exponential consensus in dynamic networks via uniform complete observability theory. IEEE Transactions on Automatic Control, 2020, 66(10): 4975−4981
    [39] Ma Q, Qin J, Anderson B D O, et al. Exponential consensus of multiple agents over dynamic network topology: Controllability, connectivity, and compactness. IEEE Transactions on Automatic Control, 2023, 68(12): 7104−7119 doi: 10.1109/TAC.2023.3245021
    [40] Qin J, Gao H, Zheng W X. Second-order consensus for multi-agent systems with switching topology and communication delay. Systems & Control Letters, 2011, 60(6): 390−397
    [41] Bernstein D S, Michel A N. A chronological bibliography on saturating actuators. International Journal of Robust and Nonlinear Control, 1995, 5: 375−380 doi: 10.1002/rnc.4590050502
    [42] Zhou B, Duan G, Lin Z. A parametric Lyapunov equation approach to the design of low gain feedback. IEEE Transactions on Automatic Control, 2008, 53(6): 1548−1554 doi: 10.1109/TAC.2008.921036
    [43] Su H, Chen M Z Q, Lam J, et al. Semi-global leader-following consensus of linear multi-agent systems with input saturation via low gain feedback. IEEE Transactions on Circuits and Systems I: Regular Papers, 2013, 60(7): 1881−1889 doi: 10.1109/TCSI.2012.2226490
    [44] Li Y, Xiang J, Wei W. Consensus problems for linear time-invariant multi-agent systems with saturation constraints. IET Control Theory & Applications, 2011, 5(6): 823−829
    [45] Meng Z, Zhao Z, Lin Z. On global leader-following consensus of identical linear dynamic systems subject to actuator saturation. Systems & Control Letters, 2013, 62(2): 132−142
    [46] Ren W, Beard R W. Consensus algorithms for double-integrator dynamics. Distributed Consensus in Multi-vehicle Cooperative Control: Theory and Applications, 200877−104
    [47] Zhao Z, Lin Z. Global leader-following consensus of a group of general linear systems using bounded controls. Automatica, 2016, 68: 294−304 doi: 10.1016/j.automatica.2016.01.027
    [48] Zhang Y, Jiang J. Bibliographical review on reconfigurable fault-tolerant control systems. Annual Reviews in Control, 2008, 32(2): 229−252 doi: 10.1016/j.arcontrol.2008.03.008
    [49] Davoodi M R, Khorasani K, Talebi H A, et al. Distributed fault detection and isolation filter design for a network of heterogeneous multiagent systems. IEEE Transactions on Control Systems Technology, 2013, 22(3): 1061−1069
    [50] Kashyap N, Yang C W, Sierla S, et al. Automated fault location and isolation in distribution grids with distributed control and unreliable communication. IEEE Transactions on Industrial Electronics, 2014, 62(4): 2612−2619
    [51] Teixeira A, Shames I, Sandberg H, et al. Distributed fault detection and isolation resilient to network model uncertainties. IEEE Transactions on Cybernetics, 2014, 44(11): 2024−2037 doi: 10.1109/TCYB.2014.2350335
    [52] Wang Y, Song Y, Lewis F L. Robust adaptive fault-tolerant control of multiagent systems with uncertain nonidentical dynamics and undetectable actuation failures. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3978−3988
    [53] Chen S, Ho D W C, Li L, et al. Fault-tolerant consensus of multi-agent system with distributed adaptive protocol. IEEE Transactions on Cybernetics, 2014, 45(10): 2142−2155
    [54] Tabuada P. Event-triggered real-time scheduling of stabilizing control tasks. IEEE Transactions on Automatic Control, 2007, 52(9): 1680−1685 doi: 10.1109/TAC.2007.904277
    [55] Cao M, Xiao F, Wang L. Event-based second-order consensus control for multi-agent systems via synchronous periodic event detection. IEEE Transactions on Automatic Control, 2015, 60(9): 2452−2457 doi: 10.1109/TAC.2015.2390553
    [56] Lu W, Han Y, Chen T. Synchronization in networks of linearly coupled dynamical systems via event-triggered diffusions. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(12): 3060−3069 doi: 10.1109/TNNLS.2015.2402691
    [57] Fan Y, Feng G, Wang Y, et al. Distributed event-triggered control of multi-agent systems with combinational measurements. Automatica, 2013, 49(2): 671−675 doi: 10.1016/j.automatica.2012.11.010
    [58] Garcia E, Cao Y, Casbeer D W. Decentralized event-triggered consensus with general linear dynamics. Automatica, 2014, 50(10): 2633−2640 doi: 10.1016/j.automatica.2014.08.024
    [59] Seyboth G S, Dimarogonas D V, Johansson K H. Event-based broadcasting for multi-agent average consensus. Automatica, 2013, 49(1): 245−252 doi: 10.1016/j.automatica.2012.08.042
    [60] Zhu W, Jiang Z P. Event-based leader-following consensus of multi-agent systems with input time delay. IEEE Transactions on Automatic Control, 2014, 60(5): 1362−1367
    [61] Cheng Y, Ugrinovskii V. Event-triggered leader-following tracking control for multivariable multi-agent systems. Automatica, 2016, 70: 204−210 doi: 10.1016/j.automatica.2016.04.003
    [62] Mu N, Liao X, Huang T. Event-based consensus control for a linear directed multiagent system with time delay. IEEE Transactions on Circuits and Systems Ⅱ: Express Briefs, 2014, 62(3): 281−285
    [63] Altafini C. Consensus problems on networks with antagonistic interactions. IEEE Transactions on Automatic Control, 2012, 58(4): 935−946
    [64] Cartwright D, Harary F. Structural balance: a generalization of Heider's theory. Psychological Review, 1956, 63(5): 277 doi: 10.1037/h0046049
    [65] Meng Z, Shi G, Johansson K H, et al. Behaviors of networks with antagonistic interactions and switching topologies. Automatica, 2016, 73: 110−116 doi: 10.1016/j.automatica.2016.06.022
    [66] Qin J, Yu C, Anderson B D O. On leaderless and leader-following consensus for interacting clusters of second-order multi-agent systems. Automatica, 2016, 74: 214−221 doi: 10.1016/j.automatica.2016.07.008
    [67] Qin J, Yu C. Cluster consensus control of generic linear multi-agent systems under directed topology with acyclic partition. Automatica, 2013, 49(9): 2898−2905 doi: 10.1016/j.automatica.2013.06.017
    [68] Ren L, Li M, Sun C. Semiglobal cluster consensus for heterogeneous systems with input saturation. IEEE Transactions on Cybernetics, 2019, 51(9): 4685−4694
    [69] Qin J, Ma Q, Gao H, et al. On group synchronization for interacting clusters of heterogeneous systems. IEEE Transactions on Cybernetics, 2016, 47(12): 4122−4133
    [70] Xia W, Cao M. Clustering in diffusively coupled networks. Automatica, 2011, 47(11): 2395−2405 doi: 10.1016/j.automatica.2011.08.043
    [71] Battistelli G, Chisci L, Mugnai G, et al. Consensus-based linear and nonlinear filtering. IEEE Transactions on Automatic Control, 2014, 60(5): 1410−1415
    [72] Battistelli G, Chisci L. Stability of consensus extended Kalman filter for distributed state estimation. Automatica, 2016, 68: 169−178 doi: 10.1016/j.automatica.2016.01.071
    [73] Zhang C, Qin J, Li H, et al. Consensus-based distributed two-target tracking over wireless sensor networks. Automatica, 2022, 146: 110593 doi: 10.1016/j.automatica.2022.110593
    [74] Chen Q, Yin C, Zhou J, et al. Hybrid consensus-based cubature Kalman filtering for distributed state estimation in sensor networks. IEEE Sensors Journal, 2018, 18(11): 4561−4569 doi: 10.1109/JSEN.2018.2823908
    [75] Guo M, Jayawardhana B. Simultaneous distributed localization, formation and group motion control: a distributed filter approach. IEEE Transactions on Control of Network Systems, DOI: 10.1109/TCNS.2024.33674482024
    [76] Sun W, Lv X, Qiu M. Distributed estimation for stochastic Hamiltonian systems with fading wireless channels. IEEE Transactions on Cybernetics, 2020, 52(6): 4897−4906
    [77] Chen W, Wang Z, Ding D, et al. Distributed state estimation over wireless sensor networks with energy harvesting sensors. IEEE Transactions on Cybernetics, 2022, 53(5): 3311−3324
    [78] Kalman RE. A new approach to linear filtering and prediction theory. ASME Journal of Basic Engineering, series D, 1961, 46: 35−45
    [79] Ljung L. Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems. IEEE Transactions on Automatic Control, 1979, 24(1): 36−50 doi: 10.1109/TAC.1979.1101943
    [80] Wan EA, Van Der Merwe R. The unscented Kalman filter. Kalman Filtering and Neural Networks, 2001, 1: 221−280
    [81] Julier S J, Uhlmann J K. Reduced sigma point filters for the propagation of means and covariances through nonlinear transformations. In: Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301), 2002, 2 : 887−892
    [82] Haykin S, Arasaratnam I. Cubature Kalman filters. IEEE Transactions on Automatic Control, 2009, 54(6): 1254−1269 doi: 10.1109/TAC.2009.2019800
    [83] Chen B, Hu G, Ho DW, Yu L. Distributed covariance intersection fusion estimation for cyber-physical systems with communication constraints. IEEE Transactions on Automatic Control, 2016, 61(12): 4020−4026 doi: 10.1109/TAC.2016.2539221
    [84] Yu D, Xia Y, Li L, Zhai DH. Event-triggered distributed state estimation over wireless sensor networks. Automatica, 2020, 118: 109039 doi: 10.1016/j.automatica.2020.109039
    [85] Peng H, Zeng B, Yang L, Xu Y, Lu R. Distributed extended state estimation for complex networks with nonlinear uncertainty. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34(9): 5952−5960
    [86] Wang S, Ren W, Chen J. Fully distributed dynamic state estimation with uncertain process models. IEEE Transactions on Control of Network Systems, 2017, 5(4): 1841−1851
    [87] Yu F, Dutta RG, Zhang T, Hu Y, Jin Y. Fast attack-resilient distributed state estimator for cyber-physical systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(11): 3555−65 doi: 10.1109/TCAD.2020.3013072
    [88] Zhang C, Qin J, Yan C, Shi Y, Wang Y, Li M. Towards invariant extended Kalman filter-based resilient distributed state estimation for moving robots over mobile sensor networks under deception attacks. Automatica, 2024, 159: 111408 doi: 10.1016/j.automatica.2023.111408
    [89] Xie L, Choi DH, Kar S, Poor HV. Fully distributed state estimation for wide-area monitoring systems. IEEE Transactions on Smart Grid, 2012, 3(3): 1154−1169 doi: 10.1109/TSG.2012.2197764
    [90] Qian J, Duan P, Duan Z, Shi L. Event-triggered distributed state estimation: A conditional expectation method. IEEE Transactions on Automatic Control, 2023, 68(10): 6361−6368 doi: 10.1109/TAC.2023.3234453
    [91] Duan P, Wang Q, Duan Z, Chen G. A distributed optimization scheme for state estimation of nonlinear networks with norm-bounded uncertainties. IEEE Transactions on Automatic Control, 2021, 67(5): 2582−2589
    [92] Zhang C, Qin J, Ma Q, Shi Y, Li M. Resilient distributed state estimation for LTI systems under time-varying deception attacks. IEEE Transactions on Control of Network Systems, 2022, 10(1): 381−393
    [93] Wang H, Liu K, Han D, Xia Y. Vulnerability analysis of distributed state estimation under joint deception attacks. Automatica, 2023, 157: 111274 doi: 10.1016/j.automatica.2023.111274
    [94] Facchinei F, Kanzow C. Generalized Nash equilibrium problems. Annals of Operations Research, 2010, 175(1): 177−211 doi: 10.1007/s10479-009-0653-x
    [95] Ye M, Hu G. Adaptive approaches for fully distributed Nash equilibrium seeking in networked games. Automatica, 2021, 129: 109661 doi: 10.1016/j.automatica.2021.109661
    [96] Meng Q, Nian X, Chen Y, Chen Z. Attack-resilient distributed Nash equilibrium seeking of uncertain multiagent systems over unreliable communication networks. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(5): 6365−6379
    [97] Ye M, Han Q L, Ding L, Xu S, Jia G. Distributed Nash equilibrium seeking strategies under quantized communication. IEEE/CAA Journal of Automatica Sinica, 2022, 1(1): 103−112
    [98] Zhong Y, Yuan Y, Yuan H. Nash Equilibrium Seeking for Multi-Agent Systems Under DoS Attacks and Disturbances. IEEE Transactions on Industrial Informatics, 2023, 20(4): 5395−5405
    [99] Gadjov D, Pavel L. A passivity-based approach to Nash equilibrium seeking over networks. IEEE Transactions on Automatic Control, 2018, 64(3): 1077−1092
    [100] Romano A R, Pavel L. Dynamic gradient play for NE seeking with disturbance rejection. In: Proceedings of IEEE Conference on Decision and Control (CDC), 2018. 346−351
    [101] Lou Y, Hong Y, Xie L, Shi G, Johansson K H. Nash equilibrium computation in subnetwork zero-sum games with switching communications. IEEE Transactions on Automatic Control, 2015, 61(10): 2920−2935
    [102] Lu K, Jing G, Wang L. Distributed algorithms for searching generalized Nash equilibrium of noncooperative games. IEEE Transactions on Cybernetics, 2018, 49(6): 2362−2371
    [103] Chen S, Cheng R S. Operating reserves provision from residential users through load aggregators in smart grid: A game theoretic approach. IEEE Transactions on Smart Grid, 2017, 10(2): 1588−1598
    [104] Zhu Y, Yu W, Wen G, Chen G. Distributed Nash equilibrium seeking in an aggregative game on a directed graph. IEEE Transactions on Automatic Control, 2020, 66(6): 2746−2753
    [105] Carnevale G, Fabiani F, Fele F, Margellos K, Notarstefano G. Tracking-based distributed equilibrium seeking for aggregative games. IEEE Transactions on Automatic Control, DOI: 10.1109/TAC.2024.3368967
    [106] 时侠圣, 任璐, 孙长银. 自适应分布式聚合博弈广义纳什均衡算法. 自动化学报, 2024, 50(6): 1−11

    Shi Xiasheng, Ren Lu, Sun Changyin. Distributed Adaptive Generalized Nash Equilibrium Algorithm for Aggregative Games. Acta Automatica Sinica, 2024, 50(6): 1−11
    [107] Zhang Y, Sun J, Wu C. Vehicle-to-grid coordination via mean field game. IEEE Control Systems Letters, 2021, 6: 2084−2089
    [108] Alasseur C, Ben T I, Matoussi A. An extended mean field game for storage in smart grids. Journal of Optimization Theory and Applications, 2020, 184: 644−670 doi: 10.1007/s10957-019-01619-3
    [109] Martinez P J, Quijano N, Ocampo M C. ash equilibrium seeking in full-potential population games under capacity and migration constraints. Automatica, 2022, 141: 110285 doi: 10.1016/j.automatica.2022.110285
    [110] Zhang J, Lu J, Cao J, Huang W, Guo J, Wei Y. Traffic congestion pricing via network congestion game approach. Discrete & Continuous Dynamical Systems: Series A, 2021, 41(7): 1553−1567
    [111] Zeng J, Wang Q, Liu J, Chen J, Chen H. A potential game approach to distributed operational optimization for microgrid energy management with renewable energy and demand response. IEEE Transactions on Industrial Electronics, 2018, 66(6): 4479−4489
    [112] Deng Z, Luo J. Distributed algorithm for nonsmooth multi-coalition games and its application in electricity markets. Automatica, 2024, 161: 111494 doi: 10.1016/j.automatica.2023.111494
    [113] Meng M, Li X. On the linear convergence of distributed Nash equilibrium seeking for multi-cluster games under partial-decision information. Automatica, 2023, 151: 110919 doi: 10.1016/j.automatica.2023.110919
    [114] Basar T, Olsder G J. Dynamic noncooperative game theory. San Diego: Academic, 1999
    [115] Modares H, Lewis F L, Jiang Z P. $H_{\infty}$ tracking control of completely unknown continuous-time systems via off-policy reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(10): 2550−2562 doi: 10.1109/TNNLS.2015.2441749
    [116] Song R, Lewis F L, Wei Q. Off-policy integral reinforcement learning method to solve nonlinear continuous-time multiplayer nonzero-sum games. IEEE Transactions on Neural Networks and Learning Systems, 2016, 28(3): 704−713
    [117] Odekunle A, Gao W, Davari M, Jiang Z P. Reinforcement learning and non-zero-sum game output regulation for multi-player linear uncertain systems. Automatica, 2020, 112: 108672 doi: 10.1016/j.automatica.2019.108672
    [118] Li M, Qin J, Freris N M, Ho D W C. Multiplayer Stackelberg–Nash game for nonlinear system via value iteration-based integral reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2020, 33(4): 1429−1440
    [119] Mukaidani H, Xu H. Stackelberg strategies for stochastic systems with multiple followers. Automatica, 2015, 53: 53−59 doi: 10.1016/j.automatica.2014.12.021
    [120] 李曼, 秦家虎, 王龙. 线性二次二人 Stackelberg 博弈均衡点求解: 一 种 Q 学习方法. 中国科学: 信息科学, 2022, 52(6): 1083−1097 doi: 10.1360/SSI-2021-0016

    Li M, Qin J, Wang L. Seeking equilibrium for linear-quadratic two-player Stackelberg game: A Q-learning approach. SCIENTIA SINICA Informationis, 2022, 52(6): 1083−1097 doi: 10.1360/SSI-2021-0016
    [121] Lin Y. Necessary/sufficient conditions for Pareto optimality in finite horizon mean-field type stochastic differential game. Automatica, 2020, 119: 108951 doi: 10.1016/j.automatica.2020.108951
    [122] Vamvoudakis K G, Lewis F L, Hudas G R. Multi-agent differential graphical games: Online adaptive learning solution for synchronization with optimality. Automatica, 2012, 48(8): 1598−1611 doi: 10.1016/j.automatica.2012.05.074
    [123] Jiao Q, Modares H, Xu S, Lewis F L, Vamvoudakis K G. Multi-agent zero-sum differential graphical games for disturbance rejection in distributed control. Automatica, 2016, 69: 24−34 doi: 10.1016/j.automatica.2016.02.002
    [124] Li M, Qin J, Ma Q, Zheng W X, Kang Y. Hierarchical optimal synchronization for linear systems via reinforcement learning: A Stackelberg–Nash game perspective. IEEE Transactions on Neural Networks and Learning Systems, 2020, 32(4): 1600−1611
    [125] Li M, Qin J, Wang Y, Kang Y. Bio-inspired dynamic collective choice in large-population systems: A robust mean-field game perspective. IEEE Transactions on Neural Networks and Learning Systems, 2020, 33(5): 1914−1924
    [126] Kamalapurkar R, Klotz J R, Walters P, Dixon W E. Model-based reinforcement learning in differential graphical games. IEEE Transactions on Control of Network Systems, 2016, 5(1): 423−433
    [127] Li J, Modares H, Chai T, Lewis F L, Xie L. Off-policy reinforcement learning for synchronization in multiagent graphical games. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(10): 2434−2445 doi: 10.1109/TNNLS.2016.2609500
    [128] Qin J, Li M, Shi Y, Ma Q, Zheng W X. Optimal synchronization control of multiagent systems with input saturation via off-policy reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2018, 30(1): 85−96
    [129] 孙长银, 穆朝絮. 多智能体深度强化学习的若干关键科学问题. 自动化学报, 2020, 46(7): 1301−1312

    Sun Changyin, Mu Chaoxu. Important Scientific Problems of Multi-Agent Deep Reinforcement Learning. Acta Automatica Sinica, 2020, 46(7): 1301−1312
    [130] Arslan G, Y{ü}ksel S. Decentralized Q-learning for stochastic teams and games. IEEE Transactions on Automatic Control, 2016, 62(4): 1545−1558
    [131] Shao J, Lou Z, Zhang H, Jiang Y, He S, Ji X. Self-organized group for cooperative multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 2022, 35: 5711−5723
    [132] Wang L, Zhang Y, Hu Y, Wang W, Zhang C, Gao Y, Hao J, Lv T, Fan C. Individual reward assisted multi-agent reinforcement learning. International Conference on Machine Learning, 202223417−23432
    [133] Leonardos S, Overman W, Panageas I, Piliouras G. Global convergence of multi-agent policy gradient in markov potential games. arXiv preprint arXiv: 2106.01969, 2021
    [134] Zhang K, Hu B, Basar T. On the stability and convergence of robust adversarial reinforcement learning: A case study on linear quadratic systems. Advances in Neural Information Processing Systems, 2020, 33: 22056−22068
    [135] Yang Y, Luo R, Li M, Zhou M, Zhang W, Wang J. Mean field multi-agent reinforcement learning. International Conference on Machine Learning, 20185571−5580
    [136] Ben P E. Rationality, Nash equilibrium and backwards induction in perfect-information games. The Review of Economic Studies, 1997, 64(1): 23−46 doi: 10.2307/2971739
    [137] Brown N, Sandholm T. Reduced space and faster convergence in imperfect-information games via pruning. International Conference on Machine Learning, 2017596−604
    [138] Lowe R, Wu Y, Tamar A, Harb J, Abbeel O P, Mordatch I. Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, 2017, 30: 6379−6390
    [139] Sunehag P, Lever G, Gruslys A, Czarnecki W, Zambaldi V, Jaderberg M, Lanctot M, et al. Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward. Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 20182085−2087
    [140] Rashid T, Samvelyan M, De W, Christian S, Farquhar G, Foerster J, Whiteson S. Monotonic value function factorisation for deep multi-agent reinforcement learning. Journal of Machine Learning Research, 2020, 21(178): 1−51
    [141] Ruan J, Du Y, Xiong X, Xing D, Li X, Meng L, Zhang H, Wang J, Xu B. GCS: Graph-Based Coordination Strategy for Multi-Agent Reinforcement Learning. Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 20221128−1136
    [142] Li X, Li J, Shi H, Hwang K S. A Decentralized Communication Framework based on Dual-Level Recurrence for Multi-Agent Reinforcement Learning. IEEE Transactions on Cognitive and Developmental Systems, 2023, 16(2): 640−649
    [143] Jiang H, Ding Z, Lu Z. Settling Decentralized Multi-Agent Coordinated Exploration by Novelty Sharing. arXiv preprint arXiv: 2402.02097, 2024
    [144] Wang H, Yu Y, Jiang Y. A Fully decentralized multiagent communication via causal inference. IEEE Transactions on Neural Networks and Learning Systems, 2022, 34(12): 10193−10202
    [145] van Goor P, Mahony R. EqVIO: An equivariant filter for visual-inertial odometry. IEEE Transactions on Robotics, 2023, 39(5): 3567−3585 doi: 10.1109/TRO.2023.3289587
    [146] Shan T, Englot B, Meyers D, et al. Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. In: Proceeding of 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020: 5135−5142
    [147] Shan T, Englot B, Ratti C, et al. Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping. In: Proceeding of 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021: 5692−5698
    [148] Zhang Z, Wang L, Zhou L, et al. Learning spatial-context-aware global visual feature representation for instance image retrieval. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023: 11250−11259
    [149] Harris C, Stephens M. A combined corner and edge detector. In: Proceedings of the 4th Alvey Vision Conference, 1988, 147−151
    [150] Fang S, Li H. Multi-Vehicle Cooperative Simultaneous LiDAR SLAM and Object Tracking in Dynamic Environments. IEEE Transactions on Intelligent Transportation Systems, DOI: 10.1109/TITS.2024.3360259
    [151] Zhang C, Qin J, Yan C, et al. Towards invariant extended Kalman filter-based resilient distributed state estimation for moving robots over mobile sensor networks under deception attacks. Automatica, 2024, 159: 111408 doi: 10.1016/j.automatica.2023.111408
    [152] Zhang Z, Wang S, Hong Y, et al. Distributed dynamic map fusion via federated learning for intelligent networked vehicles. In: Proceeding of 2021 IEEE International conference on Robotics and Automation (ICRA), IEEE, 2021: 953−959
    [153] Khamis A, Hussein A, Elmogy A. Multi-robot task allocation: A review of the state-of-the-art. Cooperative robots and sensor networks, 201531−51
    [154] Choi HL, Brunet L, How JP. Consensus-based decentralized auctions for robust task allocation. IEEE transactions on robotics, 2009, 25(4): 912−26 doi: 10.1109/TRO.2009.2022423
    [155] Bai X, Fielbaum A, Kronmüller M, Knoedler L, Alonso-Mora J. Group-based distributed auction algorithms for multi-robot task assignment. IEEE Transactions on Automation Science and Engineering, 2022, 20(2): 1292−1303
    [156] Park S, Zhong YD, Leonard NE. Multi-robot task allocation games in dynamically changing environments. IEEE International Conference on Robotics and Automation (ICRA), 20218678−8684
    [157] Shorinwa O, Haksar RN, Washington P, Schwager M. Distributed multirobot task assignment via consensus ADMM. IEEE Transactions on Robotics, 2023, 39(3): 1781−800 doi: 10.1109/TRO.2022.3228132
    [158] Williams Z, Chen J, Mehr N. Distributed potential ilqr: Scalable game-theoretic trajectory planning for multi-agent interactions. IEEE International Conference on Robotics and Automation (ICRA), 202301−07
    [159] Soria E, Schiano F, Floreano D. Predictive control of aerial swarms in cluttered environments. Nature Machine Intelligence, 2021, 3(6): 545−554 doi: 10.1038/s42256-021-00341-y
    [160] Saravanos AD, Aoyama Y, Zhu H, Theodorou EA. Distributed differential dynamic programming architectures for large-scale multiagent control. IEEE Transactions on Robotics, 2023, 39(6): 4387−4407 doi: 10.1109/TRO.2023.3319894
    [161] Yao W, de Marina HG, Sun Z, Cao M. Guiding vector fields for the distributed motion coordination of mobile robots. IEEE Transactions on Robotics, 2022, 39(2): 1119−35
    [162] Chen Y, Guo M, Li Z. Deadlock resolution and recursive feasibility in MPC-based multi-robot trajectory generation. IEEE Transactions on Automatic Control, 2024, DOI: 10.1109/TAC.2024.3393126
    [163] Spica R, Cristofalo E, Wang Z, Montijano E, Schwager M. A real-time game theoretic planner for autonomous two-player drone racing. IEEE Transactions on Robotics, 2020, 36(5): 1389−1403 doi: 10.1109/TRO.2020.2994881
    [164] Chen M, Shih JC, Tomlin CJ. Multi-vehicle collision avoidance via hamilton-jacobi reachability and mixed integer programming. IEEE 55th Conference on Decision and Control, 20161695−1700
    [165] Li M, Qin J, Li J, Liu Q, Shi Y, Kang Y. Game-Based Approximate Optimal Motion Planning for Safe Human-Swarm Interaction. IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2023.3340659
    [166] Fan T, Long P, Liu W, Pan J. Distributed multi-robot collision avoidance via deep reinforcement learning for navigation in complex scenarios. The International Journal of Robotics Research, 2020, 39(7): 856−892 doi: 10.1177/0278364920916531
    [167] Xie Z, Dames P. Drl-vo: Learning to navigate through crowded dynamic scenes using velocity obstacles. IEEE Transactions on Robotics, 2023, 39(4): 2700−2719 doi: 10.1109/TRO.2023.3257549
    [168] Han R, Chen S, Wang S, Zhang Z, Gao R, Hao Q, Pan J. Reinforcement learned distributed multi-robot navigation with reciprocal velocity obstacle shaped rewards. IEEE Robotics and Automation Letters, 2022, 7(3): 5896−5903 doi: 10.1109/LRA.2022.3161699
    [169] Chen L, Wang Y, Miao Z, Feng M, Zhou Z, Wang H, Wang D. Reciprocal Velocity Obstacle Spatial-Temporal Network for Distributed Multirobot Navigation. IEEE Transactions on Industrial Electronics, 2024, DOI: 10.1109/TIE.2024.3379630
    [170] Qin J, Qin J, Qiu J, Liu Q, Li M, Ma Q. SRL-ORCA: A Socially Aware Multi-Agent Mapless Navigation Algorithm in Complex Dynamic Scenes. IEEE Robotics and Automation Letters, 2023, 9(1): 143−150
    [171] Brito B, Everett M, How JP, Alonso-Mora J. Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments. IEEE Robotics and Automation Letters, 2021, 6(3): 4616−4623 doi: 10.1109/LRA.2021.3068662
    [172] He Z, Dong L, Song C, Sun C. Multiagent soft actor-critic based hybrid motion planner for mobile robots. IEEE transactions on neural networks and learning systems, 2022, 34(12): 10980−10992
    [173] Zhu K, Zhang T. Deep reinforcement learning based mobile robot navigation: A review. Tsinghua Science and Technology, 2021, 26(5): 674−691 doi: 10.26599/TST.2021.9010012
    [174] Li Y, Davis C, Lukszo Z, Weijnen M. Electric vehicle charging in China's power system: Energy, economic and environmental trade-offs and policy implications. Applied Energy, 2016, 173: 535−554 doi: 10.1016/j.apenergy.2016.04.040
    [175] Chandra I, Singh N K, Samuel P. A comprehensive review on coordinated charging of electric vehicles in distribution networks. Journal of Energy Storage, 2024, 89: 111659 doi: 10.1016/j.est.2024.111659
    [176] Franco J F, Rider M J, Romero R. A mixed-integer linear programming model for the electric vehicle charging coordination problem in unbalanced electrical distribution systems. IEEE Transactions on Smart Grid, 2015, 6(5): 2200−2210 doi: 10.1109/TSG.2015.2394489
    [177] Das R, Wang Y, Busawon K, Putrus G, Neaimeh M. Real-time multi-objective optimisation for electric vehicle charging management. Journal of Cleaner Production, 2021, 292: 126066 doi: 10.1016/j.jclepro.2021.126066
    [178] Wan Y, Qin J, Yu X, Yang T, Kang Y. Price-based residential demand response management in smart grids: A reinforcement learning-based approach. IEEE/CAA Journal of Automatica Sinica, 2022, 9(1): 123−134 doi: 10.1109/JAS.2021.1004287
    [179] Zhang P, Qian K, Zhou C, Stewart B G, Hepburn D M. A methodology for optimization of power systems demand due to electric vehicle charging load. IEEE Transactions on Power Systems, 2012, 27(3): 1628−1636 doi: 10.1109/TPWRS.2012.2186595
    [180] Ioakimidis C S, Thomas D, Rycerski P, Genikomsakis K N. Peak shaving and valley filling of power consumption profile in non-residential buildings using an electric vehicle parking lot. Energy, 2018, 148: 148−158 doi: 10.1016/j.energy.2018.01.128
    [181] Van Kriekinge G, De Cauwer C, Sapountzoglou N, Coosemans T, Messagie M. Peak shaving and cost minimization using model predictive control for uni- and bi-directional charging of electric vehicles. Energy Reports, 2021, 7: 8760−8771 doi: 10.1016/j.egyr.2021.11.207
    [182] Gong J, Fu W, Kang Yu, Qin J, Xiao F. Multi-agent deep reinforcement learning based multi-objective charging control for electric vehicle charging station. In Chinese Conference on Swarm Intelligence and Cooperative Control. Nanjing, China, 2023. 266−277
    [183] Tu R, Gai Y J, Farooq B, Posen D, Hatzopoulou M. Electric vehicle charging optimization to minimize marginal greenhouse gas emissions from power generation. Applied Energy, 2020, 277: 115517 doi: 10.1016/j.apenergy.2020.115517
    [184] Adetunji K E, Hofsajer I W, Abu-Mahfouz A M, Cheng L. An optimization planning framework for allocating multiple distributed energy resources and electric vehicle charging stations in distribution networks. Applied Energy, 2022, 322: 119513 doi: 10.1016/j.apenergy.2022.119513
    [185] Ran L, Qin J, Wan Y, Fu W, Yu W, Xi ao, F. Fast charging navigation strategy of EVs in power-transportation networks: A coupled network weighted pricing perspective. IEEE Transactions on Smart Grid, 2024, 15(4): 3864−3875 doi: 10.1109/TSG.2024.3354300
    [186] Wan Y, Qin J, Li F, Yu X, Kang Y. Game theoretic-based distributed charging strategy for PEVs in a smart charging station. IEEE Transactions on Smart Grid, 2021, 12(1): 538−547 doi: 10.1109/TSG.2020.3020466
    [187] Zhang L, Li Y. A game-theoretic approach to optimal scheduling of parking-lot electric vehicle charging. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4068−4078 doi: 10.1109/TVT.2015.2487515
    [188] Kabir M E, Assi C, Tushar M H K, Yan J. Optimal scheduling of EV charging at a solar power-based charging station. IEEE Systems Journal, 2021, 14(3): 4221−4231
    [189] Zavvos E, Gerding E H, Brede M. A comprehensive game-theoretic model for electric vehicle charging station competition. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 12239−12250 doi: 10.1109/TITS.2021.3111765
    [190] Chen J, Huang X, Cao Y, Li L, Yan K, Wu L, Liang K. Electric vehicle charging schedule considering shared charging pile based on generalized Nash game. International Journal of Electrical Power & Energy Systems, 2022, 136: 107579
    [191] Yan D, Yin H, Li T, Ma C. A two-stage scheme for both power allocation and EV charging coordination in a grid-tied PV–battery charging station. IEEE Transactions on Industrial Informatics, 2021, 17(10): 6994−7004 doi: 10.1109/TII.2021.3054417
    [192] Liu Z, Wu Q, Huang S, Wang L, Shahidehpour M, Xue Y. Optimal day-ahead charging scheduling of electric vehicles through an aggregative game model. IEEE Transactions on Smart Grid, 2018, 9(5): 5173−5184 doi: 10.1109/TSG.2017.2682340
    [193] Lin R, Chu H, Gao J, Chen H. Charging management and pricing strategy of electric vehicle charging station based on mean field game theory. Asian Journal of Control, 2024, 26(2): 803−813 doi: 10.1002/asjc.3173
    [194] Wang Y, Wang X, Shao C, Gong N. Distributed energy trading for an integrated energy system and electric vehicle charging stations: A Nash bargaining game approach. Renewable Energy, 2020, 155: 513−530 doi: 10.1016/j.renene.2020.03.006
    [195] Pahlavanhoseini A, Sepasian M S. Optimal planning of PEV fast charging stations using Nash bargaining theory. Journal of Energy Storage, 2019, 25: 100831 doi: 10.1016/j.est.2019.100831
    [196] Ran L, Wan Y, Qin J, Fu W, Zhang D, Kang Y. A game-based battery swapping station recommendation approach for electric vehicles. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(9): 9849−9860 doi: 10.1109/TITS.2023.3269570
    [197] Zeng H, Sheng Y, Sun H, Zhou Y, Xue Y, Guo Q. A conic relaxation approach for solving Stackelberg pricing game of electric vehicle charging station considering traffic equilibrium. IEEE Transactions on Smart Grid, 2024, 15(3): 3080−3097 doi: 10.1109/TSG.2023.3329651
    [198] Wan Y, Qin J, Ma Q, Fu W, Wang S. Multi-agent DRL-based data-driven approach for PEVs charging/discharging scheduling in smart grid. Journal of the Franklin Institute, 2022, 359: 1747−1767 doi: 10.1016/j.jfranklin.2022.01.016
    [199] Zhang Z, Wan Y, Qin J, Fu W, Kang Yu. A deep RL-based algorithm for coordinated charging of electric vehicles. IEEE Transactions on Intelligent Transportation System, 2022, 23(10): 18774−18784 doi: 10.1109/TITS.2022.3170000
    [200] Park K, Moon I. Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid. Applied energy, 2022, 328: 120111 doi: 10.1016/j.apenergy.2022.120111
    [201] Zhang Y, Yang Q, An D, Li D, Wu Z. Multistep multiagent reinforcement learning for optimal energy schedule strategy of charging stations in smart grid. IEEE Transactions on Cybernetics, 2023, 53(7): 4292−305 doi: 10.1109/TCYB.2022.3165074
    [202] Liang Y, Ding Z, Zhao T, Lee W J. Real-time operation management for battery swapping-charging system via multi-agent deep reinforcement learning. IEEE Transactions on Smart Grid, 2023, 14(1): 559−571 doi: 10.1109/TSG.2022.3186931
    [203] Wang L, Liu S, Wang P, Xu L, Hou L, Fei A. QMIX-based multi-agent reinforcement learning for electric vehicle-Facilitated peak shaving. In 2023 IEEE Global Communications Conference. Kuala Lumpur, Malaysia, 2023. 1693−1698
  • 加载中
计量
  • 文章访问数:  297
  • HTML全文浏览量:  206
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-07-16
  • 录用日期:  2024-11-06
  • 网络出版日期:  2024-12-13

目录

    /

    返回文章
    返回