A Unified Optimization for Resilient Dynamic Event-Triggering Consensus Under Denial of Service

• Published in: IEEE transactions on cybernetics Vol. 52; no. 5; pp. 2872 - 2884
• Main Authors: Amini, Amir, Asif, Amir, Mohammadi, Arash
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Co-design; Consensus; Convergence; Convexity; Denial of service attacks; denial-of-service (DoS) attack; dynamic event triggering (DET); Estimation; Exact solutions; Mathematical analysis; Multiagent systems; multiagent systems (MASs); Optimization; Protocols; Resilience; Symmetric matrices; Upper bound; Upper bounds; Vehicle dynamics
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2020.3022568

This article proposes a resilient framework for optimized consensus using a dynamic event-triggering (DET) scheme, where the multiagent system (MAS) is subject to denial-of-service (DoS) attacks. When initiated by an adversary, DoS blocks the local and neighboring communication channels in the network. A distributed DET scheme is utilized to limit transmissions between the neighboring agents. A novel convex optimization approach is proposed that simultaneously co-designs all unknown control and DET parameters. The optimization is based on the weighted sum approach and increases the interevent interval for a predefined consensus convergence rate. In the presence of DoS, the proposed co-design framework is beneficial in two ways: 1) the desired level of resilience to DoS is included as a given (desired) input and 2) the upper bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) compared to those obtained from other analytical solutions. A structured tradeoff between relevant features of the MAS, namely, the consensus convergence rate, frequency of event triggerings, and level of resilience to DoS attacks, is established. Simulations based on nonholonomic mobile robots quantify the effectiveness of the proposed implementation.