Resilient predictive control strategy of cyber–physical systems against FDI attack

Self-triggered control strategy has been widely applied in cyber–physical systems, which are unavoidable to suffer various malicious cyber-attacks. This paper is aimed at solving the cyber security problem of self-triggered model predictive control in cyber–physical systems under false data injection attack, and a resilient control strategy is proposed based on control signal reconstruction. Firstly, the continuous control signal is discretely sampled at the controller side, and the key data samples are determined and protected. Then, the discretised control samples are transmitted through the network channel, based on which the control signal is reconstructed at the controlled system side according to a preset mode. It is further theoretically proved that the control signals reconstructed from the critical control samples can guarantee the feasibility and stability of the control system against the FDI attacks. Finally, a simulation verification on a robot system is conducted to verify the effectiveness of the proposed algorithm.