Full Text:   <271>

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CLC number: TP13

On-line Access: 2024-02-23

Received: 2023-06-28

Revision Accepted: 2024-02-23

Crosschecked: 2023-11-06

Cited: 0

Clicked: 473

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Chongrong FANG

https://orcid.org/0000-0003-2357-0228

Jianping HE

https://orcid.org/0000-0002-6253-7802

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Frontiers of Information Technology & Electronic Engineering  2024 Vol.25 No.2 P.182-196

http://doi.org/10.1631/FITEE.2300467


Towards resilient average consensus inmulti-agent systems: a detection and compensation approach


Author(s):  Chongrong FANG, Wenzhe ZHENG, Zhiyu HE, Jianping HE, Chengcheng ZHAO, Jingpei WANG

Affiliation(s):  Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; more

Corresponding email(s):   crfang@sjtu.edu.cn, wzzheng@sjtu.edu.cn, hzy970920@sjtu.edu.cn, jphe@sjtu.edu.cn, chengchengzhao@zju.edu.cn, wjp@csu.ac.cn

Key Words:  Resilient consensus, Multi-agent systems, Malicious attacks, Detection, Compensation


Chongrong FANG, Wenzhe ZHENG, Zhiyu HE, Jianping HE, Chengcheng ZHAO, Jingpei WANG. Towards resilient average consensus inmulti-agent systems: a detection and compensation approach[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(2): 182-196.

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Abstract: 
Consensus is one of the fundamental distributed control technologies for collaboration in multi-agent systems such as collaborative handling in intelligent manufacturing. In this paper, we study the problem of resilient average consensus for multi-agent systems with misbehaving nodes. To protect consensus value from being influenced by misbehaving nodes, we address this problem by detecting misbehaviors, mitigating the corresponding adverse impact, and achieving the resilient average consensus. General types of misbehaviors are considered, including attacks, accidental faults, and link failures. We characterize the adverse impact of misbehaving nodes in a distributed manner via two-hop communication information and develop a deterministic detection compensation based consensus (D-DCC) algorithm with a decaying fault-tolerant error bound. Considering scenarios wherein information sets are intermittently available due to link failures, a stochastic extension named stochastic detection compensation based consensus (S-DCC) algorithm is proposed. We prove that D-DCC and S-DCC allow nodes to asymptotically achieve resilient accurate average consensus and unbiased resilient average consensus in a statistical sense, respectively. Then, the Wasserstein distance is introduced to analyze the accuracy of S-DCC. Finally, extensive simulations are conducted to verify the effectiveness of the proposed algorithms.

一种基于检测和补偿的多智能体系统弹性平均一致性方法

方崇荣1,郑文喆1,何志宇1,何建平1,赵成成2,汪京培3
1上海交通大学自动化系,中国上海市,200240
2浙江大学工业控制技术国家重点实验室,中国杭州市,310027
3中国科学院空间应用工程与技术中心,中国北京市,100094
摘要:一致性是多智能体系统分布式协同控制的基础技术之一,例如智能制造中的多智能体协同控制。本文研究了具有行为不当节点的多智能体系统的弹性平均一致性问题。为保护一致性的收敛值免受不当行为节点的影响,本文通过检测不当行为、减轻相应不利影响并实现弹性平均一致性来解决此问题。本文考虑一般化的不当行为,包括恶意攻击、意外故障和链路故障。基于两跳通信信息,以分布式方式描述行为不当节点的不利影响,并面向确定性系统提出一种基于检测与补偿的一致性算法(D-DCC算法),且该算法具有衰减容错错误界限。考虑到由于链路随机故障而导致信息集间歇性失效的场景,我们面向随机性系统提出一种基于检测补偿的一致性算法(S-DCC算法)。本文证明了D-DCC和S-DCC算法分别使得节点在统计意义上渐进地实现弹性准确平均一致性和无偏弹性平均一致性。紧接着,本文引入沃瑟斯坦距离来分析S-DCC的准确性。最后,进行大量仿真来验证所提算法的有效性。

关键词:弹性一致性;多智能体系统;恶意攻击;检测;补偿

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