Model-free adaptive control for nonlinear multiagent systems under hybrid cyber attack

In this paper, a novel model-free adaptive control approach is presented for a class of unknown non-affine multi-agent systems (MASs) subject to hybrid cyber attacks. Firstly, the unknown nonlinear MASs are constructed as the dynamic linearized data model. Secondly, the hybrid cyber attacks are assumed to arise in feedback channels, consisting of aperiodic denial-of-service (DoS) and persistent deception attacks, which could result in the absence or authenticity lackness of system signals sent to the controller. Then, a novel model-free adaptive control framework based on historical and online data generated by the system ia proposed, which motivates the system performance during the hybrid cyber attacks. Finally, the proposed method is validated by simulation.