# An overview of systems-theoretic guarantees in data-driven model predictive control

arXiv: https://arxiv.org/abs/2406.04130 · HTML: https://mlsanity.com/paper/2406.04130/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain](https://mlsanity.com/paper/2509.14203/) (2025)
- [STEMS: Spatial-Temporal Enhanced Safe Multi-Agent Coordination for Building Energy Management](https://mlsanity.com/paper/2510.14112/) (2025)
- [Learning to control switching nonlinear systems with Koopman operator regression](https://mlsanity.com/paper/2607.11344/) (2026)

## Abstract (arXiv)

The development of control methods based on data has seen a surge of interest in recent years. When applying data-driven controllers in real-world applications, providing theoretical guarantees for the closed-loop system is of crucial importance to ensure reliable operation. In this review, we provide an overview of data-driven model predictive control (MPC) methods for controlling unknown systems with guarantees on systems-theoretic properties such as stability, robustness, and constraint satisfaction. The considered approaches rely on the Fundamental Lemma from behavioral theory in order to predict input-output trajectories directly from data. We cover various setups, ranging from linear systems and noise-free data to more realistic formulations with noise and nonlinearities, and we provide an overview of different techniques to ensure guarantees for the closed-loop system. Moreover, we discuss avenues for future research that may further improve the theoretical understanding and practical applicability of data-driven MPC.