# Robust Control Design for Linear Systems via Multiplicative Noise

arXiv: https://arxiv.org/abs/2004.08019 · HTML: https://mlsanity.com/paper/2004.08019/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

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

- [On Reward-Balancing Methods for Reinforcement Learning](https://mlsanity.com/paper/2604.20433/) (2026)

## Abstract (arXiv)

Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.