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.

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A novel self-triggered model predictive control (STMPC) strategy for linear constrained systems with additive disturbances is presented in this paper. The actuator adopts the control input from the controller by sample-and-hold fashion. At the same time, the triggering threshold is designed to ensure the feasibility of the algorithms and the stability of the closed-loop system. Furthermore, two different ways to generate the event triggered input signal, namely using single sample and multiple samples, are proposed and it is shown that better triggering performance can be obtained as the number of samples increases. In the last, the effectiveness and applicability of the proposed methods are verified by simulation.

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.