Toward Optimal False Data Injection Attack against Self‐Triggered Model Predictive Controllers. Issue 6 (13th March 2022) - He, Ning; Ma, Kai; Li, Ruoxia
Abstract: This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC) systems with sample‐and‐hold input signals to address the potential security defects. First, the mathematical model of FDI attack against the considered STMPC system is established. Then, the difference between the states of the nominal system and the attacked system is explicitly calculated such that the impact of FDI attacks on the STMPC systems can be quantitatively analyzed. And finally, an efficient and effective algorithm to realize the desired FDI attack is proposed, and in order to maintain the flexibility of the attacker, the designed FDI attack algorithm is developed under different attacking scenarios, including attacking a single control node at each sampling time and attacking multiple control nodes each time. Finally, two simulation experiments are carried out based on a robot system and a cart–damper–spring system to verify the efficacy and optimality of the designed FDI attack strategy. Abstract : This paper is aimed at exploring the optimal false data injection (FDI) attack against continuous time self‐triggered model predictive control (STMPC). The proposed method can effectively generate the worst FDI attack that destabilize the targeted system with a negligibly small amount of online computation, which help address the potential security defects of the STMPC system for generating effective countermeasures.