Journal
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
Volume 68, Issue 8, Pages 4722-4737Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2022.3209399
Keywords
Index Terms-Data-driven control; denial-of-service at-tack; input-to-state stability; model predictive control
Ask authors/readers for more resources
This article presents a data-driven model predictive control scheme for resilient control of linear time-invariant systems under denial-of-service attacks, achieving similar resilience as model-based control methods.
The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on precollected input-output trajectories (i.e., without explicit system models). In this article, we take an initial step toward data-driven stabilization of LTI systems under DoS attacks, and develop a resilient model predictive control scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available