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Trusted-Region Subsequence Reduction for Designing Resilient Consensus Algorithms
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DOI：10.1109/TNSE.2020.3036755.png)
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摘要
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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 f malicious agents in the entire network or each neighborhood (i.e., f-total or f-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 f-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.
Keyword:
Consensus algorithm
Topology
Convergence
Germanium
Upper bound
Task analysis
Mechanical engineering
Multi-agent network
resilient consensus
malicious attack
trusted-region subsequence reduction
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