Publication
Using Learned Flow-Matching Surrogate Models for Adaptive Receding-Horizon Control
Philipp Holzmann; Maik Pfefferkorn; Jan Peters; Richard D. Braatz; Rolf Findeisen
In: European Control Conference, ECC 2026, Reykjavík, Iceland, July 7-10, 2026. European Control Conference (ECC), Pages 1459-1465, IEEE, 2026.
Abstract
Learning-based surrogate models offer a powerful
alternative to analytical models for model-based control of non-
linear dynamical systems with uncertain and context-dependent
dynamics. A receding-horizon control framework is developed
that exploits flow-matching models to generate state trajectories
conditioned on the system’s initial state, uncertain parameters,
and candidate input sequences. These models provide expres-
sive, data-driven surrogate dynamics without requiring explicit
analytical representations. To compute control inputs, Bayesian
optimization minimizes a cost function evaluated on surrogate-
generated trajectories, enabling efficient optimization despite
the non-differentiable and computationally expensive nature of
the generative model. The resulting inputs are applied in a
receding-horizon fashion and re-optimized using updated state
and parameter information, yielding an adaptive, learning-
based control strategy. The effectiveness of the approach is
demonstrated on a bioreactor system.