DOS攻击下饱和脉冲多智能体系统的安全定制化一致性

胡翔; 熊余; 张祖凡

doi:10.16383/j.aas.c240203

DOS攻击下饱和脉冲多智能体系统的安全定制化一致性

doi: 10.16383/j.aas.c240203 cstr: 32138.14.j.aas.c240203

胡翔^1,,
熊余^1,,
张祖凡^2,

1.
重庆邮电大学自动化学院重庆 400065
2.
重庆邮电大学通信与信息工程学院重庆 400065

基金项目: 国家自然科学基金(62377007), 重庆市教委科技研究计划重大项目(KJZD-M201900601)资助

详细信息

作者简介:
胡翔：重庆邮电大学讲师. 2022年获得重庆邮电大学博士学位. 主要研究方向为多智能体协同与智能控制技术. 本文通信作者. E-mail: huxiang@cqupt.edu.cn

熊余：重庆邮电大学研究员. 2014年获得重庆大学博士学位. 主要研究方向为模式识别与机器学习. E-mail: xiongyu@cqupt.edu.cn

张祖凡：重庆邮电大学教授. 2007年获得电子科技大学博士学位. 主要研究方向为无线移动通信理论与技术. E-mail: zhangzf@cqupt.edu.cn

计量
- 文章访问数: 217
- HTML全文浏览量: 72
- PDF下载量: 36
- 被引次数: 0
出版历程
- 收稿日期: 2024-04-12
- 录用日期: 2024-06-28
- 网络出版日期: 2024-07-22
- 刊出日期: 2024-12-20

Security Customization Consensus of Multi-agent Systems Based on Saturation Impulse Under DOS Attacks

1.
School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065
2.
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065

Funds: Supported by National Natural Science Foundation of China (62377007) and Major Project of Science and Technology Research Program of Chongqing Education Commission (KJZD-M201900601)

More Information

Author Bio:
HU Xiang　Lecturer at Chongqing University of Posts and Telecommunications. He received his Ph.D. degree from Chongqing University of Posts and Telecommunications in 2022. His research interest covers multi-agent collaboration and intelligent control technology. Corresponding author of this paper

XIONG Yu　Researcher at Chongqing University of Posts and Telecommunications. He received his Ph.D. degree from Chongqing University in 2014. His research interest covers pattern recognition and machine learning

ZHANG Zu-Fan　Professor at Chongqing University of Posts and Telecommunications. He received his Ph.D. degree from University of Electronic Science and Technology of China in 2007. His research interest covers wireless mobile communication theory and technology

摘要

摘要: 提出并解决一种饱和脉冲多智能体系统在拒绝服务(Denial of service, DOS)攻击环境中的安全定制化一致性控制问题. 首先引入微分机制和加权策略, 构建一种带可调参数一致性模式项的系统模型, 以满足复杂场景对一致性的定制化需求. 其次结合饱和效应和脉冲机制, 为系统设计一种满足执行器功率受限约束的饱和脉冲控制协议. 再次采用切换拓扑分析DOS攻击下系统的网络拓扑结构, 并采用李雅普洛夫稳定性和矩阵测度理论, 得到系统实现安全定制化一致性的充分条件. 最后通过仿真实验和对比分析, 验证了所提理论的有效性和优越性.
- 多智能体系统 /
- 安全定制化一致性 /
- 饱和脉冲 /
- 拒绝服务攻击 /
- 切换拓扑
Abstract: This paper proposes and solves the problem of security customization consensus control for multi-agent systems based on saturation impulse in denial of service (DOS) attack environment. Firstly, the differential mechanism and weighting strategy are introduced to construct a system model that contains a consensus schema item with adjustable parameters, so as to meet the customization requirements for consensus in some complex scenarios. Secondly, combined with saturation effect and impulse mechanism, a saturation impulse control protocol is designed for the system, which satisfies the constraint of actuators power. Thirdly, some switching topologies are used to analyze the network topologies of the system under DOS attacks, and the sufficient conditions for the system to achieve security customization consensus are obtained by using Lyapunov stability and matrix measure theories. Finally, through some simulation experiments and comparative analysis, the validity and superiority of the proposed theories are verified.
- Multi-agent systems /
- security customization consensus /
- saturation impulse /
- denial of service (DOS) attack /
- switching topologies

HTML全文

图 1 DOS攻击模型

Fig. 1 DOS attacks model

下载: 全尺寸图片幻灯片

图 2 实验1网络拓扑情况

Fig. 2 The network topologies of experiment 1

下载: 全尺寸图片幻灯片

图 3 实验1脉冲时刻分布

Fig. 3 The impulse time distribution of experiment 1

下载: 全尺寸图片幻灯片

图 4 实验1误差系统状态值

Fig. 4 The state values of error system in experiment 1

下载: 全尺寸图片幻灯片

图 5 实验1智能体节点状态值

Fig. 5 The state values of agent nodes in experiment 1

下载: 全尺寸图片幻灯片

图 13 实验3智能体节点状态值

Fig. 13 The state values of agent nodes in experiment 3

下载: 全尺寸图片幻灯片

图 6 实验2网络拓扑情况

Fig. 6 The network topologies of experiment 2

下载: 全尺寸图片幻灯片

图 7 实验2脉冲时刻分布

Fig. 7 The impulse time distribution of experiment 2

下载: 全尺寸图片幻灯片

图 8 实验2误差系统状态值

Fig. 8 The state values of error system in experiment 2

下载: 全尺寸图片幻灯片

图 9 实验2智能体节点状态值

Fig. 9 The state values of agent nodes in experiment 2

下载: 全尺寸图片幻灯片

图 10 实验3网络拓扑情况

Fig. 10 The network topologies of experiment 3

下载: 全尺寸图片幻灯片

图 11 实验3脉冲时刻分布

Fig. 11 The impulse time distribution of experiment 3

下载: 全尺寸图片幻灯片

图 12 实验3误差系统状态值

Fig. 12 The state values of error system in experiment 3

下载: 全尺寸图片幻灯片

图 14 消融实验1智能体节点状态值

Fig. 14 The state values of agent nodes in ablation experiment 1

下载: 全尺寸图片幻灯片

图 15 消融实验2智能体节点状态值

Fig. 15 The state values of agent nodes in ablation experiment 2

下载: 全尺寸图片幻灯片

表 1 定制化一致性模式

Table 1 Customization consensus schemes

序号	参数取值	一致性模式种类
1	$({{\varepsilon _1} = 1}) \land ({{\varepsilon _2} = 0})$	平均一致性模式
2	$({{\varepsilon _1} = 0}) \land ({{\varepsilon _2} = 1})$	领导跟随一致性模式
3	$({1 > {\varepsilon _1} > 0}) \land ({{\varepsilon _2} > 0})$	混合一致性模式

下载: 导出CSV

参考文献(24)

[1]	Lu K L, Xu H, Zheng Y S. Distributed resource allocation via multi-agent systems under time-varying networks. Automatica, 2022, 136: Article No. 110059 doi: 10.1016/j.automatica.2021.110059
[2]	Zheng Y L, Liu Q S. A review of distributed optimization: Problems, models and algorithms. Neurocomputing, 2022, 483: 446−459 doi: 10.1016/j.neucom.2021.06.097
[3]	柴天佑. 工业人工智能发展方向. 自动化学报, 2020, 46(10): 2005−2012 Chai Tian-You. Development directions of industrial artificial intelligence. Acta Automatica Sinica, 2020, 46(10): 2005−2012
[4]	Gong Z, Nie Z G, Liu Q, Liu X J. Design and control of a multi-mobile-robot cooperative transport system based on a novel six degree-of-freedom connector. ISA Transactions, 2023, 139: 606−620 doi: 10.1016/j.isatra.2023.04.006
[5]	Li K Q, Wang J W, Zheng Y. Cooperative formation of autonomous vehicles in mixed traffic flow: Beyond platooning. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(9): 15951−15966 doi: 10.1109/TITS.2022.3146612
[6]	Yu K T, Li Y M, Peng Z H, Tong S C. Distributed event-triggered formation control of planar vehicles without velocity sensors. IEEE Transactions on Vehicular Technology, 2023, 72(3): 2988−3000 doi: 10.1109/TVT.2022.3218706
[7]	Mohammad H, Mohammad B. Adaptive output stationary average consensus for heterogeneous unknown linear multi-agent systems. IET Control Theory and Applications, 2018, 12(7): 847−856 doi: 10.1049/iet-cta.2017.0877
[8]	Tian Y, Li H Q, Han Q. Finite-time average consensus of directed second-order multi-agent systems with Markovian switching topology and impulsive disturbance. Neural Computing and Applications, 2023, 35: 8575−8588 doi: 10.1007/s00521-022-08131-2
[9]	Hu W F, Cheng Y, Yang C H. Leader-following consensus of linear multi-agent systems via reset control: A time-varying systems approach. Automatica, 2023, 149: Article No. 110824 doi: 10.1016/j.automatica.2022.110824
[10]	Wang K P, Ding D, Tang Z, Feng J W. Leader-following consensus of nonlinear multi-agent systems with hybrid delays: Distributed impulsive pinning strategy. Applied Mathematics and Computation, 2022, 424: Article No. 127031 doi: 10.1016/j.amc.2022.127031
[11]	Huang W C, Tian B B, Liu T T, Wang J H, Liu Z Q. Event-triggered leader-following consensus of multi-agent systems under semi-Markov switching topology with partially unknown rates. Journal of the Franklin Institute, 2022, 359(7): 3103−3125 doi: 10.1016/j.jfranklin.2022.02.024
[12]	Lui D, Petrillo A, Santini S. Bipartite tracking consensus for high-order heterogeneous uncertain nonlinear multi-agent systems with unknown leader dynamics via adaptive fully-distributed PID control. IEEE Transactions on Network Science and Engineering, 2023, 10(2): 1131−1142 doi: 10.1109/TNSE.2022.3229752
[13]	Wu Y H, Wang Z P, Zhang H, Huang C. Output-based event-triggered consensus of general linear multi-agent systems with communication delay under directed graphs. Journal of the Franklin Institute, 2020, 357(6): 3702−3720 doi: 10.1016/j.jfranklin.2020.02.036
[14]	Qu M H, Wang Q D, Wei C L. Event-triggered consensus for second-order multi-agent systems with sampled position data via impulsive control. Journal of the Franklin Institute, 2022, 359(17): 9989−10016 doi: 10.1016/j.jfranklin.2022.09.023
[15]	Wang K P, Tang Z, Park J H, Feng J W. Impulsive time window based quasi-consensus on stochastic nonlinear multi-agent systems. IEEE Transactions on Network Science and Engineering, 2022, 9(5): 3602−3613 doi: 10.1109/TNSE.2022.3185307
[16]	Hu Z H, Mu X W. Impulsive consensus of stochastic multi-agent systems under semi-Markovian switching topologies and application. Automatica, 2023, 150: Article No. 110871 doi: 10.1016/j.automatica.2023.110871
[17]	He W L, Chen G R, Han Q L, Feng Q. Network-based leader-following consensus of nonlinear multi-agent systems via distributed impulsive control. Information Sciences, 2017, 380: 145−158 doi: 10.1016/j.ins.2015.06.005
[18]	Ma L, Wang Y L, Fei M R, Pan Q K. Cross-dimensional formation control of second-order heterogeneous multi-agent systems. ISA Transactions, 2022, 127: 188−196 doi: 10.1016/j.isatra.2022.02.036
[19]	Yang R, Liu S, Tan Y Y, Zhang Y J, Jiang W. Consensus analysis of fractional-order nonlinear multi-agent systems with distributed and input delays. Neurocomputing, 2019, 329: 46−52 doi: 10.1016/j.neucom.2018.10.045
[20]	晋守博, 魏章志, 李耀红. 基于大通讯时滞的二阶多智能体系统的一致性分析. 电子与信息学报, 2021, 43(4): 1029−1034 doi: 10.11999/JEIT191009 Jin Shou-Bo, Wei Zhang-Zhi, Li Yao-Hong. Consensus analysis of the second order multi-agent systems based on large communication delay. Journal of Electronics and Information Technology, 2021, 43(4): 1029−1034 doi: 10.11999/JEIT191009
[21]	Wang X, Mou S S, Anderson B. Consensus-based distributed optimization enhanced by integral feedback. IEEE Transactions on Automatic Control, 2023, 68(3): 1894−1901 doi: 10.1109/TAC.2022.3169179
[22]	Zhang T Y, Xu Y, Sun J. Dynamic event-triggered strategies for tracking control of directed multi-agent systems with Lipschitz nonlinear dynamics. International Journal of Robust and Nonlinear Control, 2022, 32(14): 8147−8162 doi: 10.1002/rnc.6280
[23]	Hu T S, Lin Z L. Control Systems With Actuator Saturation: Analysis and Design. Berlin: Springer Science and Business Media, 2001. 56−92
[24]	Desoer C, Vidyasagar M. Feedback Systems: Input-output Properties. New York: Academic Press, 1975. 85−116