Quantized attack-resilient control for Markov jump systems with sensor and actuator attacks

With the networking of control systems, cyberattacks on industrial control systems are becoming more and more frequent. In this study, the quantized attack-resilient control problem for Markov jump systems with sensor and actuator attacks and partly unknown transition rates was investigated. In the existing work, the forms of attack signals are mostly assumed directly. In order to release these assumptions, the mode-dependent monitor and logarithmic quantizer are designed to obtain the forms of attack signals and their constraints. Based on the constraints of attack signals, an adaptive filter is proposed to estimate the real system outputs, which are attacked by cyberattacks. Then, the adaptive compensator and quantized attack-resilient controller are designed to guarantee stochastic stability and H ∞ performance. Benefiting from the quantized attack-resilient control strategy, the proposed method can well combat the attacks, not only reducing the impact of the attacks, but also constraining the attack signals. Finally, an application example is presented to illustrate the obtained results.