Distributed Model-Free Adaptive Control for Learning Nonlinear MASs Under DoS Attacks

This article addresses the distributed model-free adaptive control (DMFAC) problem for learning nonlinear multiagent systems (MASs) subjected to denial-of-service (DoS) attacks. An improved dynamic linearization method is proposed to obtain an equivalent linear data model for learning systems. To alleviate the influence of DoS attacks, an attack compensation mechanism is developed. Based on the equivalent linear data model and the attack compensation mechanism, a novel learning-based DMFAC algorithm is developed to resist DoS attacks, which provides a unified framework to solve the leaderless consensus control, the leader-following consensus control, and the containment control problems. Finally, simulation examples are shown to illustrate the effectiveness of the developed DMFAC algorithm.

Automated summary

This article addresses the problem of distributed model-free adaptive control for learning nonlinear multiagent systems subjected to denial-of-service attacks. An improved dynamic linearization method is proposed to obtain an equivalent linear data model, and an attack compensation mechanism is developed to alleviate the influence of DoS attacks. A novel learning-based DMFAC algorithm is developed based on the equivalent linear data model and the attack compensation mechanism to resist DoS attacks, providing a unified framework to solve various control problems.