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arXiv · 2603.15063

Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation

Renato Quartullo·Andrea Garulli·Mirko Leomanni
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Abstract

This paper presents a new data-driven robust predictive control law, for linear systems affected by unknown-but-bounded process disturbances. A sequence of input-state data is used to construct a suitable uncertainty representation based on interval matrices. Then, the effect of uncertainty along the prediction horizon is bounded through an operator leveraging matrix zonotopes. This yields a tube that is exploited within a variable-horizon optimal control problem, to guarantee robust satisfaction of state and input constraints. The resulting data-driven predictive control scheme is proven to be recursively feasible and practically stable. A numerical example shows that the proposed approach compares favorably to existing methods based on zonotopic tubes.

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Renato Quartullo, Andrea Garulli, Mirko Leomanni. 2026-03-16. Data-Driven Robust Predictive Control with Interval Matrix Uncertainty Propagation. https://arxiv.org/abs/2603.15063

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