LAUSR

This article considers the probabilistic‐constrained tracking problem of a networked nonlinear system subject to hybrid attacks on the sensor and actuator channels. Two independent Markov‐based attack models are proposed for characterizing a mixture of denial‐of‐service, deception and replay attacks. We generalize the attack models in the sense that Markov processes are more practical and complicated than Bernoulli ones, and the attacks at the actuator end are also considered, which prevents the control input from reaching the actuator. A resilient receding‐horizon control law is designed based on the seriously tampered output, consisting of a probability‐based observer and a convex optimization procedure for control parameters. It is capable of mitigating the attacks on both channels and fulfilling the tracking tasks by establishing a trade‐off between the volume of the constrained set and the violation probability of the tracking error.