Gain-varying P-type ILC for Nonlinear Multi-agent Systems against FDI Attacks

This work studies the consensus learning control problem of nonlinear multi-agent systems (MASs) under false data injection (FDI) attacks. Applying the attacked output and consensus error information to the control law and the parameter updating algorithm effectively reduces the impact of FDI attacks. The provided control scheme, which is entirely data-driven and more suited for real-world industrial applications, makes no use of any model knowledge from the controlled MASs. In addition, compared with the P-type ILC method, the variable gain effectively improves the robustness of the presented GV-PILC scheme to the external uncertainties. The simulation results verify the effectiveness of the proposed method by comparing with fixed gain P-type ILC.