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

arXiv: https://arxiv.org/abs/2110.12766 · HTML: https://mlsanity.com/paper/2110.12766/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [Multi-agent Embodied AI: Advances and Future Directions](https://mlsanity.com/paper/2505.05108/) (2025)
- [Sampling Efficient Deep Reinforcement Learning through Preference-Guided Stochastic Exploration](https://mlsanity.com/paper/2206.09627/) (2022)
- [Boosting Black-Box Adversarial Attacks with Meta Learning](https://mlsanity.com/paper/2203.14607/) (2022)
- [Resilient Learning-Based Control Under Denial-of-Service Attacks](https://mlsanity.com/paper/2409.07766/) (2024)

## Abstract (arXiv)

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.