Robust Model Predictive Control via Conformal Prediction

Felix Czaja, Daniil Kazantsev, Eyke Hüllermeier
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:1096-1098, 2026.

Abstract

Model predictive control (Rawlings et al., 2017) is an established and widely used method in control theory. More specifically, it is a feedback control strategy that uses a dynamic model of the system under control to predict its future behavior over a finite time horizon. At each time step, it solves an optimization problem to determine the best control action, based on the predicted system behavior. In real applications, the prediction of the future system behavior is commonly afflicted with uncertainty. In this work, we therefore consider the use of conformal prediction (Vovk et al., 2005) to increase safety and make control more robust. Broadly speaking, the idea is to conformalize the predicted system behavior, replacing precise trajectories by confidence bands that cover the true behavior with high probability. Safe control inputs can then be selected in a more “cautious” way by optimizing worst-case scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-czaja26a, title = {Robust Model Predictive Control via Conformal Prediction}, author = {Czaja, Felix and Kazantsev, Daniil and H{\"u}llermeier, Eyke}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {1096--1098}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/czaja26a/czaja26a.pdf}, url = {https://proceedings.mlr.press/v329/czaja26a.html}, abstract = {Model predictive control (Rawlings et al., 2017) is an established and widely used method in control theory. More specifically, it is a feedback control strategy that uses a dynamic model of the system under control to predict its future behavior over a finite time horizon. At each time step, it solves an optimization problem to determine the best control action, based on the predicted system behavior. In real applications, the prediction of the future system behavior is commonly afflicted with uncertainty. In this work, we therefore consider the use of conformal prediction (Vovk et al., 2005) to increase safety and make control more robust. Broadly speaking, the idea is to conformalize the predicted system behavior, replacing precise trajectories by confidence bands that cover the true behavior with high probability. Safe control inputs can then be selected in a more “cautious” way by optimizing worst-case scenarios.} }
Endnote
%0 Conference Paper %T Robust Model Predictive Control via Conformal Prediction %A Felix Czaja %A Daniil Kazantsev %A Eyke Hüllermeier %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-czaja26a %I PMLR %P 1096--1098 %U https://proceedings.mlr.press/v329/czaja26a.html %V 329 %X Model predictive control (Rawlings et al., 2017) is an established and widely used method in control theory. More specifically, it is a feedback control strategy that uses a dynamic model of the system under control to predict its future behavior over a finite time horizon. At each time step, it solves an optimization problem to determine the best control action, based on the predicted system behavior. In real applications, the prediction of the future system behavior is commonly afflicted with uncertainty. In this work, we therefore consider the use of conformal prediction (Vovk et al., 2005) to increase safety and make control more robust. Broadly speaking, the idea is to conformalize the predicted system behavior, replacing precise trajectories by confidence bands that cover the true behavior with high probability. Safe control inputs can then be selected in a more “cautious” way by optimizing worst-case scenarios.
APA
Czaja, F., Kazantsev, D. & Hüllermeier, E.. (2026). Robust Model Predictive Control via Conformal Prediction. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:1096-1098 Available from https://proceedings.mlr.press/v329/czaja26a.html.

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