自适应分布式聚合博弈广义纳什均衡算法

时侠圣; 任璐; 孙长银

doi:10.16383/j.aas.c230584

自适应分布式聚合博弈广义纳什均衡算法

doi: 10.16383/j.aas.c230584

时侠圣^{1, 2, 3,},
任璐^{1, 2, 3,},
孙长银^{1, 2, 3,}

1.
安徽大学自主无人系统技术教育部工程研究中心合肥 230601
2.
安徽大学安徽省无人系统与智能技术工程研究中心合肥 230601
3.
安徽大学人工智能学院合肥 230601

基金项目: 国家自然科学基金创新研究群体科学基金(61921004), 国家自然科学基金重点项目(62236002, 62136008), 国家自然科学基金(62303009)资助

详细信息

作者简介:
时侠圣：安徽大学人工智能学院博士后. 2020年获得浙江大学控制科学与控制工程专业博士学位. 主要研究方向为分布式协同优化和网络化系统. E-mail: shixiasheng@zju.edu.cn

任璐：安徽大学人工智能学院讲师. 2021年获得东南大学控制科学与工程专业博士学位. 主要研究方向为多智能体系统一致性控制, 复杂动态网络的同步. E-mail: penny_lu@ahu.edu.cn

孙长银：安徽大学人工智能学院教授. 1996年获得四川大学应用数学专业学士学位. 分别于2001年, 2004年获得东南大学电子工程专业硕士和博士学位. 主要研究方向为智能控制, 飞行器控制, 模式识别和优化理论. 本文通信作者. E-mail: cysun@seu.edu.cn

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出版历程
- 收稿日期: 2023-09-19
- 录用日期: 2024-01-23
- 网络出版日期: 2024-03-12
- 刊出日期: 2024-06-27

Distributed Adaptive Generalized Nash Equilibrium Algorithm for Aggregative Games

SHI Xia-Sheng^{1, 2, 3
,},
REN Lu^{1, 2, 3
,},
SUN Chang-Yin^{1, 2, 3
,}

1.
Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, Anhui University, Hefei 230601
2.
Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology, Anhui University, Hefei 230601
3.
School of Artificial Intelligence, Anhui University, Hefei 230601

Funds: Supported by Foundation for Innovative Research Groups of National Natural Science Foundation of China (61921004), Key Projects of National Natural Science Foundation of China (62236002, 62136008), and National Natural Science Foundation of China (62303009)

More Information

Author Bio:
SHI Xia-Sheng　Postdoctor at the School of Artificial Intelligence, Anhui University. He received his Ph.D. degree in control science and control engineering from Zhejiang University in 2020. His research interest covers distributed cooperative optimization and network system

REN Lu　Lecturer at the School of Artificial Intelligence, Anhui University. She received her Ph.D. degree in control science and engineering from Southeast University in 2021. Her research interest covers consensus control of multi-agent systems and synchronization of complex dynamical networks

SUN Chang-Yin　Professor at the School of Artificial Intelligence, Anhui University. He received his bachelor degree in applied mathematics from Sichuan University in 1996, and his master and Ph.D. degrees in electrical engineering from Southeast University in 2001 and 2004, respectively. His research interest covers intelligent control, flight control, pattern recognition, and optimal theory. Corresponding author of this paper

摘要

摘要: 随着信息物理系统技术的发展, 面向多智能体系统的分布式协同优化问题得到广泛研究. 主要研究面向多智能体系统的受约束分布式聚合博弈问题, 其中局部智能体成本函数受到全局聚合项约束和全局等式耦合约束. 首先, 面向一阶积分型多智能体系统设计一种基于估计梯度下降的纳什均衡求解算法. 其中, 利用多智能体系统平均一致性方法设计一种自适应估计策略, 以实现全局聚合项约束分布式估计, 并据此计算出梯度函数估计值. 其次, 利用状态反馈策略和输出反馈策略将上述算法推广至状态信息可测和状态信息不可测一般线性异构多智能体系统. 最后, 利用拉萨尔不变性原理证实上述算法收敛性, 并提供多组案例仿真用以验证算法有效性.
- 聚合博弈 /
- 自适应 /
- 比例积分 /
- 梯度跟踪 /
- 一般线性多智能体系统
Abstract: With the development of cyber-physical system technology, the distributed cooperative optimization problem for multi-agent systems has been widely studied. This study focuses on the distributed constrained aggregative game for multi-agent systems, where the local cost function is subject to the global aggregative and global equality constraints. Firstly, a Nash equilibrium seeking algorithm based on estimation gradient descent is designed for the first-order integrator-based multi-agent systems. To this end, an adaptive estimation scheme is designed using the average consensus method of multi-agent systems to realize the distributed estimation of global aggregative function. Based on this, the estimation gradient function is calculated. Secondly, the above algorithm is extended to the state-accessible and state-inaccessible general heterogeneous linear multi-agent systems using the state and output feedback control scheme, respectively. Finally, the convergence proof is provided using the LaSalle＇s invariance principle and several simulation examples are provided for illustrating the effectiveness of our proposed algorithms.
- Aggregative game /
- adaptive /
- proportional-integral /
- gradient tracking /
- general linear multi-agent system

HTML全文

图 1 本文算法的状态$ x_i$轨迹

Fig. 1 The state trajectories $ x_i$in our algorithm

下载: 全尺寸图片幻灯片

图 2 本文算法的自适应权重$ \alpha_{ij}$轨迹

Fig. 2 The trajectories of the adaptive weight $ \alpha_{ij}$ in our algorithm

下载: 全尺寸图片幻灯片

图 3 不同算法的输出收敛误差轨迹

Fig. 3 The output convergence error trajectories of different algorithms

下载: 全尺寸图片幻灯片

图 4 不同控制参数值下本文算法的输出收敛误差轨迹

Fig. 4 The output convergence error trajectories of our algorithm under different control parameters

下载: 全尺寸图片幻灯片

图 5 本文算法的输出$ y_i$轨迹

Fig. 5 The output trajectories $ y_i$ in our algorithm

下载: 全尺寸图片幻灯片

图 6 本文算法的状态观测误差轨迹

Fig. 6 The trajectories of state observer error in our algorithm

下载: 全尺寸图片幻灯片

参考文献(51)

[1]	Cornes R. Aggregative environmental games. Environmental and Resource Economics, 2016, 63(2): 339−365 doi: 10.1007/s10640-015-9900-6
[2]	Barrera J, Garcia A. Dynamic incentives for congestion control. IEEE Transactions on Automatic Control, 2015, 60(2): 299−310 doi: 10.1109/TAC.2014.2348197
[3]	耿远卓, 袁利, 黄煌, 汤亮. 基于终端诱导强化学习的航天器轨道追逃博弈. 自动化学报, 2023, 49(5): 974−984 Geng Yuan-Zhuo, Yuan Li, Huang Huang, Tang Liang. Terminal-guidance based reinforcement-learning for orbital pursuit-evasion game of the spacecraft. Acta Automatica Sinica, 2023, 49(5): 974−984
[4]	Ye M J, Han Q L, Ding L, Xu S Y. Distributed Nash equilibrium seeking in games with partial decision information: A survey. Proceedings of the IEEE, 2023, 111(2): 140−157 doi: 10.1109/JPROC.2023.3234687
[5]	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 Long, Huang Feng. An interdisciplinary survey of multi-agent games, learning, and control. Acta Automatica Sinica, 2023, 49(3): 580−613
[6]	陈灵敏, 冯宇, 李永强. 基于距离信息的追逃策略: 信念状态连续随机博弈. 自动化学报, 2024, 50(4): 828−840 Chen Ling-Min, Feng Yu, Li Yong-Qiang. Distance information based pursuit-evasion strategy: Continuous stochastic game with belief state. Acta Automatica Sinica, 2024, 50(4): 828−840
[7]	Koshal J, Nedić A, Shanbhag U V. Distributed algorithms for aggregative games on graphs. Operations Research, 2016, 64(3): 680−704 doi: 10.1287/opre.2016.1501
[8]	Grammatico S. Dynamic control of agents playing aggregative games with coupling constraints. IEEE Transactions on Automatic Control, 2017, 62(9): 4537−4548 doi: 10.1109/TAC.2017.2672902
[9]	Huang S J, Lei J L, Hong Y G. A linearly convergent distributed Nash equilibrium seeking algorithm for aggregative games. IEEE Transactions on Automatic Control, 2023, 68(3): 1753−1759 doi: 10.1109/TAC.2022.3154356
[10]	Ye M J, Hu G Q, Xie L H, Xu S Y. Differentially private distributed Nash equilibrium seeking for aggregative games. IEEE Transactions on Automatic Control, 2022, 67(5): 2451−2458 doi: 10.1109/TAC.2021.3075183
[11]	Shi C X, Yang G H. Distributed Nash equilibrium computation in aggregative games: An event-triggered algorithm. Information Sciences, 2019, 489: 289−302 doi: 10.1016/j.ins.2019.03.047
[12]	Parise F, Gentile B, Lygeros J. A distributed algorithm for almost-Nash equilibria of average aggregative games with coupling constraints. IEEE Transactions on Control of Network Systems, 2020, 7(2): 770−782 doi: 10.1109/TCNS.2019.2944300
[13]	Sun C, Hu G Q. Nash equilibrium seeking with prescribed performance. Control Theory and Technology, 2023, 21(3): 437−447 doi: 10.1007/s11768-023-00169-4
[14]	Belgioioso G, Nedic A, Grammatico S. Distributed generalized Nash equilibrium seeking in aggregative games on time-varying networks. IEEE Transactions on Automatic Control, 2021, 66(5): 2061−2075 doi: 10.1109/TAC.2020.3005922
[15]	Pan W, Xu X L, Lu Y, Zhang W D. Distributed Nash equilibrium learning for average aggregative games: Harnessing smoothness to accelerate the algorithm. IEEE Systems Journal, 2023, 17(3): 4855−4865 doi: 10.1109/JSYST.2023.3264791
[16]	Zhang P, Yuan Y, Liu H P, Gao Z. Nash equilibrium seeking for graphic games with dynamic event-triggered mechanism. IEEE Transactions on Cybernetics, 2022, 52(11): 12604−12611 doi: 10.1109/TCYB.2021.3071746
[17]	Fang X, Wen G H, Zhou J L, Lv J H, Chen G R. Distributed Nash equilibrium seeking for aggregative games with directed communication graphs. IEEE Transactions on Circuits and Systems I: Regular Papers, 2022, 69(8): 3339−3352 doi: 10.1109/TCSI.2022.3168770
[18]	Shi X L, Wen G H, Yu X H. Finite-time convergent algorithms for time-varying distributed optimization. IEEE Control Systems Letters, 2023, 7: 3223−3228 doi: 10.1109/LCSYS.2023.3312297
[19]	时侠圣, 杨涛, 林志赟, 王雪松. 基于连续时间的二阶多智能体分布式资源分配算法. 自动化学报, 2021, 47(8): 2050−2060 Shi Xia-Sheng, Yang Tao, Lin Zhi-Yun, Wang Xue-Song. Distributed resource allocation algorithm for second-order multi-agent systems in continuous-time. Acta Automatica Sinica, 2021, 47(8): 2050−2060
[20]	An L W, Yang G H. Distributed optimal coordination for heterogeneous linear multiagent systems. IEEE Transactions on Automatic Control, 2022, 67(12): 6850−6857 doi: 10.1109/TAC.2021.3133269
[21]	Shi J, Ye M J. Distributed optimal formation control for unmanned surface vessels by a regularized game-based approach. IEEE/CAA Journal of Automatica Sinica, 2024, 11(1): 276−278 doi: 10.1109/JAS.2023.123930
[22]	王鼎. 一类离散动态系统基于事件的迭代神经控制. 工程科学学报, 2022, 44(3): 411−419 Wang Ding. Event-based iterative neural control for a type of discrete dynamic plant. Chinese Journal of Engineering, 2022, 44(3): 411−419
[23]	王鼎. 基于学习的鲁棒自适应评判控制研究进展. 自动化学报, 2019, 45(6): 1031−1043 Wang Ding. Research progress on learning-based robust adaptive critic control. Acta Automatica Sinica, 2019, 45(6): 1031−1043
[24]	Ye M J, Hu G Q. Adaptive approaches for fully distributed Nash equilibrium seeking in networked games. Automatica, 2021, 129: Article No. 109661 doi: 10.1016/j.automatica.2021.109661
[25]	Ye M J. Distributed Nash equilibrium seeking for games in systems with bounded control inputs. IEEE Transactions on Automatic Control, 2021, 66(8): 3833−3839 doi: 10.1109/TAC.2020.3027795
[26]	Zhang K J, Fang X, Wang D D, Lv Y Z, Yu X H. Distributed Nash equilibrium seeking under event-triggered mechanism. IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, 68(11): 3441−3445
[27]	Liu P, Xiao F, Wei B, Yu M. Nash equilibrium seeking for individual linear dynamics subject to limited communication resource. Systems and Control Letters, 2022, 161: Article No. 105162
[28]	张苗苗, 叶茂娇, 郑元世. 预设时间下的分布式优化和纳什均衡点求解. 控制理论与应用, 2022, 39(8): 1397−1406 doi: 10.7641/CTA.2022.10604 Zhang Miao-Miao, Ye Mao-Jiao, Zheng Yuan-Shi. Prescribed-time distributed optimization and Nash equilibrium seeking. Control Theory and Applications, 2022, 39(8): 1397−1406 doi: 10.7641/CTA.2022.10604
[29]	Zou Y, Huang B M, Meng Z Y, Ren W. Continuous-time distributed Nash equilibrium seeking algorithms for non-cooperative constrained games. Automatica, 2021, 127: Article No. 109535 doi: 10.1016/j.automatica.2021.109535
[30]	Zhu Y N, Yu W W, Ren W, Wen G H, Gu J P. Generalized Nash equilibrium seeking via continuous-time coordination dynamics over digraph. IEEE Transactions on Control of Network Systems, 2021, 8(2): 1023−1033 doi: 10.1109/TCNS.2021.3056034
[31]	Deng Z H, Liu Y Y, Chen T. Generalized Nash equilibrium seeking algorithm design for distributed constrained noncooperative games with second-order players. Automatica, 2022, 141: Article No. 110317 doi: 10.1016/j.automatica.2022.110317
[32]	Shi X S, Su Y X, Huang D R, Sun C Y. Distributed aggregative game for multi-agent systems with heterogeneous integrator dynamics. IEEE Transactions on Circuits and Systems II: Express Briefs, 2024, 71(4): 2169−2173
[33]	Liang S, Yi P, Hong Y G, Peng K X. Exponentially convergent distributed Nash equilibrium seeking for constrained aggregative games. Autonomous Intelligent Systems, 2022, 2(1): Article No. 6 doi: 10.1007/s43684-022-00024-4
[34]	Zhu Y N, Yu W W, Wen G H, Chen G R. Distributed Nash equilibrium seeking in an aggregative game on a directed graph. IEEE Transactions on Automatic Control, 2021, 66(6): 2746−2753 doi: 10.1109/TAC.2020.3008113
[35]	Cheng M F, Wang D, Wang X D, Wu Z G, Wang W. Distributed aggregative optimization via finite-time dynamic average consensus. IEEE Transactions on Network Science and Engineering, 2023, 10(6): 3223−3231
[36]	Wang X F, Teel A R, Sun X M, Liu K Z, Shao G R. A distributed robust two-time-scale switched algorithm for constrained aggregative games. IEEE Transactions on Automatic Control, 2023, 68(11): 6525−6540 doi: 10.1109/TAC.2023.3240981
[37]	梁银山, 梁舒, 洪奕光. 非光滑聚合博弈纳什均衡的分布式连续时间算法. 控制理论与应用, 2018, 35(5): 593−600 doi: 10.7641/CTA.2017.70617 Liang Yin-Shan, Liang Shu, Hong Yi-Guang. Distributed continuous-time algorithm for Nash equilibrium seeking of nonsmooth aggregative games. Control Theory & Applications, 2018, 35(5): 593−600 doi: 10.7641/CTA.2017.70617
[38]	Deng Z H, Nian X H. Distributed algorithm design for aggregative games of disturbed multiagent systems over weight-balanced digraphs. International Journal of Robust and Nonlinear Control, 2018, 28(17): 5344−5357 doi: 10.1002/rnc.4316
[39]	Lin W T, Chen G, Li C J, Huang T W. Distributed generalized Nash equilibrium seeking: A singular perturbation-based approach. Neurocomputing, 2022, 482: 278−286 doi: 10.1016/j.neucom.2021.11.073
[40]	Deng Z H, Nian X H. Distributed generalized Nash equilibrium seeking algorithm design for aggregative games over weight-balanced digraphs. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(3): 695−706 doi: 10.1109/TNNLS.2018.2850763
[41]	Liang S, Yi P, Hong Y G. Distributed Nash equilibrium seeking for aggregative games with coupled constraints. Automatica, 2017, 85: 179−185 doi: 10.1016/j.automatica.2017.07.064
[42]	Deng Z H. Distributed generalized Nash equilibrium seeking algorithm for nonsmooth aggregative games. Automatica, 2021, 132: Article No. 109794 doi: 10.1016/j.automatica.2021.109794
[43]	Zhang Y W, Liang S, Wang X H, Ji H B. Distributed Nash equilibrium seeking for aggregative games with nonlinear dynamics under external disturbances. IEEE Transactions on Cybernetics, 2020, 50(12): 4876−4885 doi: 10.1109/TCYB.2019.2929394
[44]	Wang X F, Sun X M, Teel A R, Liu K Z. Distributed robust Nash equilibrium seeking for aggregative games under persistent attacks: A hybrid systems approach. Automatica, 2020, 122: Article No. 109255 doi: 10.1016/j.automatica.2020.109255
[45]	Deng Z H. Distributed Nash equilibrium seeking for aggregative games with second-order nonlinear players. Automatica, 2022, 135: Article No. 109980 doi: 10.1016/j.automatica.2021.109980
[46]	Deng Z H, Liang S. Distributed algorithms for aggregative games of multiple heterogeneous Euler-Lagrange systems. Automatica, 2019, 99: 246−252 doi: 10.1016/j.automatica.2018.10.041
[47]	Deng Z H. Distributed algorithm design for aggregative games of Euler-Lagrange systems and its application to smart grids. IEEE Transactions on Cybernetics, 2022, 52(8): 8315−8325 doi: 10.1109/TCYB.2021.3049462
[48]	Liu X Y, Zhang Y W, Wang X H, Ji H B. Distributed Nash equilibrium seeking design in network of uncertain linear multi-agent systems. In: Proceedings of the 16th IEEE International Conference on Control & Automation. Sapporo, Japan: IEEE, 2020. 147−152
[49]	Li L, Yu Y, Li X X, Xie L H. Exponential convergence of distributed optimization for heterogeneous linear multi-agent systems over unbalanced digraphs. Automatica, 2022, 141: Article No. 110259 doi: 10.1016/j.automatica.2022.110259
[50]	Liu Y, Yang G H. Distributed robust adaptive optimization for nonlinear multiagent systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(2): 1046−1053 doi: 10.1109/TSMC.2019.2894948
[51]	Li S L, Nian X H, Deng Z H, Chen Z, Meng Q. Distributed resource allocation of second-order nonlinear multiagent systems. International Journal of Robust and Nonlinear Control, 2021, 31(11): 5330−5342 doi: 10.1002/rnc.5543