Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control

Robert Lefringhausen, Theodor Springer, Sandra Hirche
12/8/2025
eess.SYcs.LGmath.OC

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Abstract

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 marginal 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 both model and measurement uncertainty 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.

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Cite this paper

@article{lefringhausen2025learning,
  title  = {Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control},
  author = {Robert Lefringhausen and Theodor Springer and Sandra Hirche},
  year   = {2025},
  eprint = {2512.08013},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2512.08013v1}
}

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