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

Meijian Tan

doi:10.1109/TFUZZ.2024.3386186

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

2024-07-01

0

PRE

AI

陈晨 (C. L. Philip Chen)

吴

吴宗泽 (Zongze Wu)

DOI：10.1109/TFUZZ.2024.3386186

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Observers

Uncertainty

Consensus control

Adaptive systems

Vectors

Optimal control

Multi-agent systems

Consensus tracking control

denial of service (DoS) attacks

nonlinear multiagent systems (MASs)

resilient adaptive control

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

Abstract

Journal

Organization