Full Text:   <271>

Summary:  <80>

Suppl. Mater.: 

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


Chongrong FANG


Jianping HE


-   Go to

Article info.
Open peer comments

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


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.

@article{title="Towards resilient average consensus inmulti-agent systems: a detection and compensation approach",
author="Chongrong FANG, Wenzhe ZHENG, Zhiyu HE, Jianping HE, Chengcheng ZHAO, Jingpei WANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Towards resilient average consensus inmulti-agent systems: a detection and compensation approach
%A Chongrong FANG
%A Wenzhe ZHENG
%A Zhiyu HE
%A Jianping HE
%A Chengcheng ZHAO
%A Jingpei WANG
%J Frontiers of Information Technology & Electronic Engineering
%V 25
%N 2
%P 182-196
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300467

T1 - Towards resilient average consensus inmulti-agent systems: a detection and compensation approach
A1 - Chongrong FANG
A1 - Wenzhe ZHENG
A1 - Zhiyu HE
A1 - Jianping HE
A1 - Chengcheng ZHAO
A1 - Jingpei WANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 2
SP - 182
EP - 196
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2300467

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[1]Dibaji SM, Ishii H, Tempo R, 2018. Resilient randomized quantized consensus. IEEE Trans Autom Contr, 63(8):2508-2522.

[2]Ge XH, Han QL, Wu Q, et al., 2023. Resilient and safe platooning control of connected automated vehicles against intermittent denial-of-service attacks. IEEE/CAA J Autom Sin, 10(5):1234-1251.

[3]Gentz R, Wu SX, Wai HT, et al., 2016. Data injection attacks in randomized gossiping. IEEE Trans Signal Inform Process Netw, 2(4):523-538.

[4]Grimmett G, Stirzaker D, 2020. Probability and Random Processes. Oxford University Press, Oxford, USA.

[5]Hadjicostis CN, Domínguez-García AD, Vaidya NH, 2012. Resilient average consensus in the presence of heterogeneous packet dropping links. Proc 51st IEEE Conf on Decision Control, p.106-111.

[6]He JP, Cheng P, Shi L, et al., 2013. SATS: secure averageconsensus-based time synchronization in wireless sensor networks. IEEE Trans Signal Process, 61(24):6387-6400.

[7]He JP, Cai L, Cheng P, et al., 2019. Distributed privacypreserving data aggregation against dishonest nodes in network systems. IEEE Int Things J, 6(2):1462-1470.

[8]Kieckhafer RM, Azadmanesh MH, 1994. Reaching approximate agreement with mixed-mode faults. IEEE Trans Parall Distrib Syst, 5(1):53-63.

[9]LeBlanc HJ, Zhang HT, Koutsoukos X, et al., 2013. Resilient asymptotic consensus in robust networks. IEEE J Sel Areas Commun, 31(4):766-781.

[10]Ma RK, Zheng H, Wang JY, et al., 2022. Automatic protocol reverse engineering for industrial control systems with dynamic taint analysis. Front Inform Technol Electron Eng, 23(3):351-360.

[11]Marano S, Matta V, Tong L, 2009. Distributed detection in the presence of Byzantine attacks. IEEE Trans Signal Process, 57(1):16-29.

[12]Pasqualetti F, Bicchi A, Bullo F, 2012. Consensus computation in unreliable networks: a system theoretic approach. IEEE Trans Autom Contr, 57(1):90-104.

[13]Ramos G, Silvestre D, Silvestre C, 2022. General resilient consensus algorithms. Int J Contr, 95(6):1482-1496.

[14]Shames I, Teixeira AMH, Sandberg H, et al., 2011. Distributed fault detection for interconnected second-order systems. Automatica, 47(12):2757-2764.

[15]Vallender SS, 1974. Calculation of the Wasserstein distance between probability distributions on the line. Theory Probab Appl, 18(4):784-786.

[16]Wang W, Huang JS, Wen CY, et al., 2014. Distributed adaptive control for consensus tracking with application to formation control of nonholonomic mobile robots. Automatica, 50(4):1254-1263.

[17]Wen GH, Yu XU, Liu ZW, 2021. Recent progress on the study of distributed economic dispatch in smart grid: an overview. Front Inform Technol Electron Eng, 22(1):25-39.

[18]Xiao L, Boyd S, Lall S, 2005. A scheme for robust distributed sensor fusion based on average consensus. Proc 4th Int Symp on Information Processing in Sensor Networks, p.63-70.

[19]Xie ML, Ding DR, Ge XH, et al., 2022. Distributed platooning control of automated vehicles subject to replay attacks based on proportional integral observers. IEEE/CAA J Autom Sin, early assess.

[20]Yang FS, Liang XH, Guan XH, 2021. Resilient distributed economic dispatch of a cyber-power system under DoS attack. Front Inform Technol Electron Eng, 22(1):40-50.

[21]Yuan LW, Ishii H, 2021. Secure consensus with distributed detection via two-hop communication. Automatica, 131:109775.

[22]Zhao CC, He JP, Chen JM, 2018. Resilient consensus with mobile detectors against malicious attacks. IEEE Trans Signal Inform Process Netw, 4(1):60-69.

[23]Zheng WZ, He ZY, He JP, et al., 2021. Accurate resilient average consensus via detection and compensation. Proc 60th IEEE Conf on Decision and Control, p.5502-5507.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE