Fuzzy Adaptive Resilient Formation Control for Nonlinear Multiagent Systems Subject to DoS Attacks

• Published in: IEEE transactions on fuzzy systems Vol. 32; no. 3; pp. 1 - 9
• Main Authors: Zhou, Haodong, Tong, Shaocheng
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Artificial neural networks; Computer crime; Control methods; Denial of service attacks; Denial-of-service attack; Distributed resilient formation estimator; Formation control; Fuzzy control; Fuzzy logic; fuzzy state estimator; Fuzzy systems; Multiagent systems; Network topology; Nonlinear control; nonlinear multiagent systems; Nonlinear systems; Output feedback; Robot sensing systems; State estimation; Surface vehicles; Tracking errors
• ISSN: 1063-6706; 1941-0034
• DOI: 10.1109/TFUZZ.2023.3327140

This paper investigates the fuzzy adaptive resilient formation control issue for uncertain nonlinear multiagent systems (MASs) with immeasurable states and under denial-of service (DoS) attacks. Fuzzy logic systems (FLSs) are utilized to model unknown agents, and a fuzzy state estimator is formulated to reconstruct the agents' unknown states. Too btain the unknown leader information estimation and its high-order derivatives under DoS attacks, a distributed resilient formation estimator is proposed. Based on the designed fuzzy state estimator and resilient formation estimator, a fuzzy output-feedback adaptive resilient formation control scheme is developed via backstepping control methodology. It is proved that the developed fuzzy resilient formation control scheme can guarantee the controlled nonlinear MASs are stable and formation tracking errors converge even under unknown states and DoS attacks. Finally, the proposed fuzzy adaptive resilient formation control method is applied to marine surface vehicles (MSVs), the simulation results and comparisons show the effectiveness of the presented fuzzy adaptive resilient formation control methodology.