# Distributionally Robust LQG control under Distributed Uncertainty

arXiv: https://arxiv.org/abs/2306.05227 · HTML: https://mlsanity.com/paper/2306.05227/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2023.

## Built on by (most cited first, 2 of 2)

- [Distributionally Robust Control with End-to-End Statistically Guaranteed Metric Learning](https://mlsanity.com/paper/2510.10214/) (2025)
- [Information-Geometric First-Passage Monitoring of Distributional Stability in Stochastic Systems](https://mlsanity.com/paper/2604.24083/) (2026)

## Abstract (arXiv)

A new paradigm is proposed for the robustification of the LQG controller against distributional uncertainties on the noise process. Our controller optimizes the closed-loop performances in the worst possible scenario under the constraint that the noise distributional aberrance does not exceed a certain threshold limiting the relative entropy pseudo-distance between the actual noise distribution the nominal one. The main novelty is that the bounds on the distributional aberrance can be arbitrarily distributed along the whole disturbance trajectory. We discuss why this can, in principle, be a substantial advantage and we provide simulation results that substantiate such a principle.