Adaptive Reconstruction-Based Model Predictive Control for Networked Stochastic Systems Under False Data Injection Attacks

A resilient stochastic model predictive control (MPC) method based on an adaptive input reconstruction mechanism is proposed for networked stochastic systems under false data injection (FDI) attacks. To the best of our knowledge, this is the first stochastic MPC framework designed to address FDI attacks; it not only mitigates the conservatism of existing methods but also reduces system resource consumption. Particularly, an adaptive input reconstruction mechanism is introduced to relax the assumptions on FDI attack energy in existing resilient MPC methods by reconstructing feasible control inputs. In addition, the adaptive prediction horizon and terminal constraint are co-designed to reduce the computational complexity. Furthermore, the conservatism inherent in existing resilient MPC methods due to hard constraints is alleviated by transforming fixed hard constraints into stochastic constraints. Based on these designs, sufficient conditions are derived to guarantee the proposed method's recursive feasibility and the closed-loop system stability. Finally, the effectiveness of the proposed method is validated through simulations on a DC-DC converter system.