Adaptive Optimal Consensus Control for Nonlinear Uncertain Multiagent Systems Under DoS Attacks

• Published in: IEEE transactions on cybernetics Vol. PP; pp. 1 - 12
• Main Authors: Tan, Meijian, Liu, Zhi, Chen, Ci, Wang, Yaonan, Chen, C. L. Philip
• Format: Journal Article
• Language: English
• Published: United States IEEE 28.08.2025
• Subjects: Adaptive optimal control; Computational complexity; Consensus control; Denial-of-service attack; distributed consensus tracking; DoS attacks; Multi-agent systems; neural networks; Nonlinear dynamical systems; nonlinear MASs; Observers; Optimal control; System dynamics; Uncertainty; Vectors
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2025.3599340

This article addresses the optimal control problem for nonlinear multiagent systems (MASs) with an uncertain nonlinear leader subject to intermittent Denial-of-Service (DoS) attacks. The main challenge is estimating the leader's dynamics when the uncertain nonlinear dynamics of the leader are unknown to all followers and communication between subsystems is intermittently disrupted by attacks. Furthermore, the uncertainty in the followers' dynamics adds complexity, making it difficult to eliminate reliance on the identifier network. To overcome these challenges, we develop a learning-based adaptive distributed observer to estimate the leader's dynamics under attacks. Based on this observer, a single-critic optimal consensus tracking control scheme is proposed to solve the leader-follower consensus problem in uncertain MASs without requiring an identifier network. It is proven that all system signals are uniformly ultimately bounded (UUB), and consensus tracking is achieved. The effectiveness of the proposed method is validated through a simulation example.