# Learning-Enabled Robust Control with Noisy Measurements

arXiv: https://arxiv.org/abs/2202.08363 · HTML: https://mlsanity.com/paper/2202.08363/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [Consistent Model Chasing Is Minimax Optimal: The Exact Value of Scalar Adversarial Adaptive Control under Large Parametric Uncertainty](https://mlsanity.com/paper/2608.13651/) (2026)

## Abstract (arXiv)

We present a constructive approach to bounded ℓ2-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of mathcal H_∞-observers paired with a recursively computed performance metric. We do not assume prior knowledge of a stabilizing controller.