Robust Model Predictive Control via Conformal Prediction
MCML Authors
Eyke Hüllermeier
Prof. Dr.
Core PI
Abstract
Eyke Hüllermeier
Prof. Dr.
Core PI
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.
inproceedings CKH26
COPA 2026
15th Symposium on Conformal and Probabilistic Prediction with Applications. Mölndal, Sweden, Sep 02-04, 2026.Authors
F. Czaja • D. Kazantsev • E. HüllermeierLinks
URLResearch Area
BibTeXKey: CKH26