Data-Driven Resilient Predictive Control under Denial-of-Service

TL;DR

This paper introduces a data-driven resilient model predictive control scheme for stochastic LTI systems under DoS attacks, achieving stability without explicit system models and validated through numerical examples.

Contribution

It develops a novel data-driven control approach for stochastic LTI systems under DoS attacks, eliminating the need for explicit models and maintaining resilience.

Findings

Achieves local input-to-state stability under mild noise and attack assumptions.

Proposes modifications for global stability with trade-offs in resilience or complexity.

Validates effectiveness through numerical simulation.

Abstract

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 pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) 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.