Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163553
Title: Resilient multi-dimensional consensus in adversarial environment
Authors: Yan, Jiaqi
Li, Xiuxian
Mo, Yilin
Wen, Changyun
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Yan, J., Li, X., Mo, Y. & Wen, C. (2022). Resilient multi-dimensional consensus in adversarial environment. Automatica, 145, 110530-. https://dx.doi.org/10.1016/j.automatica.2022.110530
Project: MOE2020-T1-1-067
Journal: Automatica
Abstract: This paper considers the multi-dimensional consensus in networked systems, where some of the agents might be misbehaving (or faulty). Despite the influence of these misbehaviors, the benign agents aim to reach an agreement while avoiding being seriously influenced by the faulty ones. To this end, this paper first considers a general class of consensus algorithms, where each benign agent computes an “auxiliary point” based on the received values and moves its state towards this point. Concerning this generic form, we present conditions for achieving resilient consensus and obtain a lower bound on the exponential convergence rate. Assuming that the number of malicious agents is upper bounded, two specific resilient consensus algorithms are further developed based on the obtained conditions. Particularly, the first solution, based on Helly's Theorem, achieves the consensus within the convex hull formed by the benign agents’ initial states, where the auxiliary point can be efficiently computed through linear programming. On the other hand, the second algorithm serves as a “built-in” security guarantee for standard average consensus algorithms, in the sense that its performance coincides exactly with that of the standard ones in the absence of faulty nodes while also resisting the serious influence of the misbehaving ones in adversarial environment. Some numerical examples are provided in the end to verify the theoretical results.
URI: https://hdl.handle.net/10356/163553
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2022.110530
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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