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

• URL: http://arxiv.org/abs/2512.08013v1
• Date: Mon, 08 Dec 2025 20:10:37 GMT
• Title: Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control
• Authors: Robert Lefringhausen, Theodor Springer, Sandra Hirche,
• Abstract summary: Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available.<n>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.<n>The resulting posterior samples are used to formulate a scenario-based optimal control problem that accounts for both model and measurement uncertainty.
• Score: 4.85082036531237
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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