Learning-Based Resilient Adaptive Fuzzy Optimal Consensus for Nonlinear Multiagent Systems Under DoS Attacks

Meijian Tan, Zhi Liu, Yaonan Wang, C. L. Philip Chen, Zongze Wu

Abstract: This article addresses the learning-based resilient adaptive fuzzy optimal consensus control problem for nonlinear uncertain multiagent systems (MASs) in the presence of intermittent denial of service (DoS) attacks. A key obstacle is the uncertainty in the dynamics of the followers, which makes it challenging to eliminate dependency on the identifier network. To this end, we propose a novel critic-only optimal consensus scheme to eliminate dependency on the identifier network and significantly reduce computational complexity. Moreover, this work requires less prior knowledge and assumes that only the specific subsystems can access the leader's information under certain conditions. To cope with limited information access, we design a distributed adaptive observer to monitor the leader's dynamics. It is proven that all the signals are uniformly ultimately bounded, and consensus tracking is achieved. Finally, a simulation example is provided to demonstrate the results achieved.