Resilient MPC based on Input Reconstruction for Disturbed Nonlinear System with FDI attacks

A resilient self-triggered model predictive control (STMPC) approach based on input reconstruction is developed in this paper for disturbed nonlinear continuous-time cyber-physical system (CPS) against false data injection (FDI) attack. Firstly, an input reconstruction mechanism according to the key data is proposed based on the self-triggered aperiodic sampling characteristics and the FDI attack model to attenuate the impact of FDI attack on the controlled system. Secondly, the reconstruction parameter determination method is designed to balance the contradiction between system stability and triggering interval. Further, the stability properties of the disturbed nonlinear system based resilient STMPC algorithm are analyzed in detail. Finally, the effectiveness of the proposed resilient STMPC is verified by simulation.