An Adaptive Neural Network Approach for Resilient Leader-following Consensus Control of Multi-agent Systems Under Cyber-attacks

This paper addresses the dynamic neural networks (DNNs) based resilient leader-following consensus control of multi-agent systems (MASs) under unidentified false data injection (FDI) attacks. We have examined generic linear leader-following agents in the context of stochastic FDI attacks on the network topology. When information is sent from one agent to another, it is altered as a result of the attacks. In this study, we have introduced a new method to identify FDI attacks using DNNs. The DNNs adapt by adjusting their weights based on system errors, allowing them to approximate the nonlinear dynamics of these attacks using a state translation method for the receiving agent, as we do not have any estimate or the information of the states of the sending agent. The attacks considered in this study are network attacks, which are easier to initiate but harder to counter compared to the traditional input-output attacks. The unknown FDI attacks are estimated with the help of DNNs, which allow the evaluation and isolation of large amplitude attack signals. Unlike previous methods, this approach handles probabilistic stochastic FDI attacks and negates attack estimations from the system dynamics, enhancing the controller resilience. Additionally, the paper extends resilient consensus control to the output feedback methodology, providing a feasible consensus method for MASs under stochastic FDI attacks. Simple design constraints for the consensus control are introduced, and the approach is validated through simulations with six unmanned ground vehicles (UGVs).