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Volume 12 Issue 7
Jul.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(7): 1407-1417
J. Zhu, C. Lu, J. Li, and F.-Y. Wang, “Secure consensus control on multi-agent systems based on improved PBFT and Raft blockchain consensus algorithms,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1407–1417, Jul. 2025. doi: 10.1109/JAS.2025.125300
Citation: J. Zhu, C. Lu, J. Li, and F.-Y. Wang, “Secure consensus control on multi-agent systems based on improved PBFT and Raft blockchain consensus algorithms,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1407–1417, Jul. 2025. doi: 10.1109/JAS.2025.125300
shu

Secure Consensus Control on Multi-Agent Systems Based on Improved PBFT and Raft Blockchain Consensus Algorithms

doi: 10.1109/JAS.2025.125300
Funds:  This work was partly supported by the Fundamental Research Funds for the Central Universities (NS2024021), the Science and Technology Development Fund of Macau SAR (0145/2023/RIA3, 0093/2023/RIA2, 0050/2020/A1), and the National Natural Science Foundation of China (62103411)
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    Abstract
  • There has been significant recent research on secure control problems that arise from the open and complex real-world industrial environments. This paper focuses on addressing the issue of secure consensus control in multi-agent systems (MASs) under malicious attacks, utilizing the practical Byzantine fault tolerance (PBFT) and Raft consensus algorithm in blockchain. Unlike existing secure consensus control algorithms that have strict requirements for topology and high communication costs, our approach introduces a node grouping methodology based on system topology. Additionally, we utilize the PBFT consensus algorithm for intergroup leader identity verification, effectively reducing the communication complexity of PBFT in large-scale networks. Furthermore, we enhance the Raft algorithm through cryptographic validation during followers’ log replication, which enhances the security of the system. Our proposed consensus process not only identifies the identities of malicious agents but also ensures consensus among normal agents. Through extensive simulations, we demonstrate robust convergence, particularly in scenarios with the relaxed topological requirements. Comparative experiments also validate the algorithm’s lower consensus latency and improved efficiency compared to direct PBFT utilization for identity verification and classical secure consensus control method mean subsequence reduced (MSR) algorithm.

     

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