Robust model predictive control exploiting monotonicity properties
<|reference_start|>Robust model predictive control exploiting monotonicity properties: Robust model predictive control algorithms are essential for addressing unavoidable errors due to the uncertainty in predicting real-world systems. However, the formulation of such algorithms typically results in a trade-off between conservatism and computational complexity. Monotone systems facilitate the efficient computation of reachable sets and thus the straightforward formulation of a robust model predictive control approach optimizing over open-loop predictions. We present an approach based on the division of reachable sets to incorporate feedback in the predictions, resulting in less conservative strategies. The concept of mixed-monotonicity enables an extension of our methodology to non-monotone systems. The potential of the proposed approaches is demonstrated through a nonlinear high-dimensional chemical tank reactor cascade case study.<|reference_end|>
@article{heinlein2024robust,
    title={Robust model predictive control exploiting monotonicity properties},
    author={Moritz Heinlein, Sankaranarayanan Subramanian, Sergio Lucia},
    journal={arXiv preprint arXiv:2408.17348},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17348},
    primaryClass={eess.SY cs.SY math.OC}
}