Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162408
Title: Attack-resilient distributed convex optimization of cyber-physical systems against malicious cyber-attacks over random digraphs
Authors: Feng, Zhi
Hu, Guoqiang
Keywords: Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering
Issue Date: 2022
Source: Feng, Z. & Hu, G. (2022). Attack-resilient distributed convex optimization of cyber-physical systems against malicious cyber-attacks over random digraphs. IEEE Internet of Things Journal. https://dx.doi.org/10.1109/JIOT.2022.3201583
Journal: IEEE Internet of Things Journal
Abstract: This paper addresses a resilient exponential distributed convex optimization problem for a heterogeneous linear multi-agent system under Denial-of-Service (DoS) attacks over random digraphs. The random digraphs are caused by unreliable networks and the DoS attacks, allowed to occur aperiodically, refer to an interruption of the communication channels carried out by the intelligent adversaries. In contrast to many existing distributed convex optimization works over a perfect communication network, the global optimal solution might not be sought under the adverse influences that result in performance degradations or even failures of optimization algorithms. The aforementioned setting poses certain technical challenges to optimization algorithm design and exponential convergence analysis. In this work, several resilient algorithms are presented such that a team of agents minimizes a sum of local non-quadratic cost functions in a safe and reliable manner with the global exponential convergence. Numerical simulation results are further presented to validate the effectiveness of the proposed distributed approaches.
URI: https://hdl.handle.net/10356/162408
ISSN: 2327-4662
DOI: 10.1109/JIOT.2022.3201583
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/JIOT.2022.3201583.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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