Adaptive Learning Control for High-Order Uncertain Nonlinear Multiagent Systems under Replay Attacks

In this study, we focus on the adaptive learning control of high-order uncertain nonlinear multiagent systems (MASs) under replay attacks. The neural-network-based adaptive learning scheme is proposed to approximate the nonlinear MASs uncertainty dynamics. Under the undirected graph of MASs communication, the distributed controller and parameter estimator are designed by using the backstepping method. In particular, the boundary condition for the MASs in the presence of replay attacks is analyzed. It is shown that the follower can track the leader asymptotically under replay attacks if the boundary condition is satisfied. Simulation results demonstrate the effectiveness of the proposed theoretical approach.