Trusted-Region Subsequence Reduction for Designing Resilient Consensus Algorithms

• Published in: IEEE transactions on network science and engineering Vol. 8; no. 1; pp. 259 - 268
• Main Authors: Zhai, Yang, Liu, Zhi-Wei, Ge, Ming-Feng, Wen, Guanghui, Yu, Xinghuo, Qin, Yuzhen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Consensus algorithm; Convergence; Double integrators; Germanium; Graph theory; malicious attack; Mechanical engineering; Multi-agent network; Multiagent systems; Reduction; resilient consensus; Task analysis; Topology; trusted-region subsequence reduction; Upper bound; Upper bounds
• ISSN: 2327-4697; 2334-329X
• DOI: 10.1109/TNSE.2020.3036755

Existing resilient consensus algorithms are mainly developed based on the mean subsequence reduced (MSR) method, which relies on the assumption that there exist at most <inline-formula><tex-math notation="LaTeX">f</tex-math></inline-formula> malicious agents in the entire network or each neighborhood (i.e., <inline-formula><tex-math notation="LaTeX">f</tex-math></inline-formula>-total or <inline-formula><tex-math notation="LaTeX">f</tex-math></inline-formula>-local model). However, in some practical cases, it may be impossible to estimate an appropriate upper bound on the number of malicious agents. This paper proposes a novel method, called trusted-region subsequence reduction (TSR), for designing resilient consensus algorithm without the <inline-formula><tex-math notation="LaTeX">f</tex-math></inline-formula>-total/local model assumption. The main idea of the TSR method is to filter out the received information beyond a dynamic trusted region, determined by the current relative positions of the neighboring trusted nodes. Based on the TSR method, we design a sampled-data resilient consensus algorithm for double-integrator multi-agent networks. A necessary and sufficient graph-theoretic condition is obtained to achieve resilient consensus. Finally, simulations are conducted to illustrate the effectiveness of the proposed algorithm and the faster convergence rate of the TSR-based algorithm than the classical MSR-based algorithm.