Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this setting of limited sensing by formulating a Bayesian prior over the continuous-time dynamics and latent state trajectory in state-space form and updating it through a targeted Metropolis-Hastings sampler equipped with a numerical ODE integrator. The resulting posterior samples are used to formulate a scenario-based optimal control problem that accounts for the uncertainty in the dynamics and latent state and is solved using standard nonlinear programming methods. The approach is validated in a numerical case study on glucose regulation using a Type 1 diabetes model.
@article{arxiv.2512.08013,
title = {Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control},
author = {Robert Lefringhausen and Theodor Springer and Sandra Hirche},
journal= {arXiv preprint arXiv:2512.08013},
year = {2026}
}
Comments
Accepted for publication in the Proceedings of the 2026 IFAC World Congress