An overview of systems-theoretic guarantees in data-driven model predictive control
<|reference_start|>An overview of systems-theoretic guarantees in data-driven model predictive control: 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.<|reference_end|>
@article{berberich2024an,
    title={An overview of systems-theoretic guarantees in data-driven model
  predictive control},
    author={Julian Berberich and Frank Allg"ower},
    journal={arXiv preprint arXiv:2406.04130},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04130},
    primaryClass={eess.SY cs.SY math.OC}
}