Event-Based Human-in-the-Loop Optimal Bipartite Consensus Control for Multiagent Systems Under False Data Injection Attacks

Zongsheng Huang; Tieshan Li; Lu Liu; Hongjing Liang

doi:10.1109/JAS.2026.125918

Volume 13 Issue 9

Sep. 2026

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(9): 2119-2131

Z. Huang, T. Li, L. Liu, and H. Liang, “Event-based human-in-the-loop optimal bipartite consensus control for multiagent systems under false data injection attacks,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2119–2131, Sep. 2026. doi: 10.1109/JAS.2026.125918

Citation:

Z. Huang, T. Li, L. Liu, and H. Liang, “Event-based human-in-the-loop optimal bipartite consensus control for multiagent systems under false data injection attacks,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 9, pp. 2119–2131, Sep. 2026. doi: 10.1109/JAS.2026.125918

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Event-Based Human-in-the-Loop Optimal Bipartite Consensus Control for Multiagent Systems Under False Data Injection Attacks

doi: 10.1109/JAS.2026.125918

Funds: This work was supported in part by the National Natural Science Foundation of China (62533006, 52471376, 62322307), the Fundamental Research Funds for the Central Universities (ZYGX2024Z018), and the Sichuan Science and Technology Program (2024JDHJ0037)

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Author Bio:
Zongsheng Huang received the B.S. degree in automation from Chongqing University of Posts and Telecommunications in 2021. She is currently a Ph.D. candidate in control theory and engineering at the School of Automation Engineering, University of Electronic Science and Technology of China. Her current research interests include reinforcement learning, security control, prescribed performance control, and multiagent systems

Tieshan Li (Senior Member, IEEE) received the B.S. degree in ocean fisheries engineering from the Ocean University of China in 1992, and the Ph.D. degree in vehicle operation engineering from Dalian Maritime University (DMU) in 2005. He was a Full Professor with DMU until 2020, and a Chair Professor since 2021. From 2007 to 2015, he has held various Postdoctoral/Senior Research Associate/ Visiting Scholar positions with Shanghai Jiao Tong University, City University of Hong Kong, China, and the University of Macau, China. He is currently a Tenured Professor with the University of Electronic Science and Technology of China. His research interests include intelligent learning and control for nonlinear systems, multiagent systems, and their applications to autonomous vehicles

Lu Liu (Senior Member, IEEE) received the Ph.D. degree in mechanical and automation engineering from the Department of Mechanical and Automation Engineering, Chinese University of Hong Kong, China, in 2008. From 2009 to 2012, she was an Assistant Professor with The University of Tokyo, Japan, and then with The University of Nottingham, UK. She then joined the City University of Hong Kong, China, where she is currently a Professor. Her primary research interests include networked systems, multirobot systems, and intelligent control. Dr. Liu was an Associate Editor for the IEEE Transactions on Cybernetics, IEEE Transactions on Fuzzy Systems, IEEE Robotics and Automation Letters, Control Theory and Technology, and Unmanned Systems

Hongjing Liang (Member, IEEE) received the B.S. degree in mathematics from Bohai University in 2009, and the M.S. degree in fundamental mathematics and the Ph.D. degree in control theory and control engineering, both from Northeastern University in 2011 and 2016, respectively. He was a Temporary Research Associate with the Science Program, Texas A&M University, Oatar. He is currently a Professor with the University of Electronic Science and Technology of China. His current research interests include adaptive control, fuzzy control, and cooperative control and their applications. Dr. Liang was the recipient of the Outstanding Reviewer Award of IEEE/CAA Journal of Automatica Sinica 2019. He has been in the Editorial Board of International Journal of Fuzzy Systems, Fluctuation and Noise Letters
Corresponding author: Tieshan Li, e-mail: litieshan073@uestc.edu.cn
Received Date: 2025-07-17
Revised Date: 2025-12-03
Accepted Date: 2026-01-22

Abstract

Abstract

This work primarily addresses the dynamic event-triggered human-in-the-loop (HiTL) optimal bipartite consensus control problem for nonlinear multiagent systems (MASs) under false data injection attacks. First, a supervisor is introduced to monitor the MASs, sending commands to leader to avoid emergencies. A zero-sum game model is then developed, where the attacker and the defender are treated as adversaries. Within the unified structure of zero-sum game theory, a cost function is defined to facilitate the determination of a Nash equilibrium. Furthermore, to reduce computational resources, a dynamic event-triggered Hamilton-Jacobi-Isaacs (HJI) equation is derived, along with a dynamic event-triggered mechanism. The solution to the HJI equation is learned using the critic neural network. The boundedness of all signals in the closed-loop systems is demonstrated. Additionally, the minimal inter-event time is proven to have a lower bound, thereby excluding Zeno behavior. Finally, the simulation results confirm the validity of the proposed approach.
- Adaptive dynamic programming,
- dynamic event-triggered control,
- human-in-the-loop (HiTL) control,
- nonlinear multiagent systems (MASs),
- Zero-sum game

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References(40)

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Event-Based Human-in-the-Loop Optimal Bipartite Consensus Control for Multiagent Systems Under False Data Injection Attacks

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