Input-Behavior-Learning-Based Data Driven Control for Multiagent Systems Under DoS Attacks

This work studies Denial-of-Service (DoS) attacks for strongly connected nonaffine nonlinear multi-agent systems (MASs). At first, we develop a network topology-based linear data model (NT-LDM) to reformulate the input/output behavior among the agents. A dynamic consensus protocol is derived using the NT-LDM to enhance the consensus performance of the network topology. Meanwhile, a combined attack compensation mechanism, incorporating both input and output, is introduced for reducing the poor influence of DoS attacks on network communication. Then, an input-behavior-learning-based data-driven control (IBL-DDC) is developed to improve the consensus protocol by designing an attack compensation scheme to the unavailable data due to DoS attacks. The proposed IBL-DDC not only is independent on the system model, but also can reject DoS attacks by learning from the input action of other agents through the proposed compensation scheme. The results are confirmed by the simulation study.