# Covariance Steering for Systems Subject to Unknown Parameters

arXiv: https://arxiv.org/abs/2303.10293 · HTML: https://mlsanity.com/paper/2303.10293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2023.

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

- [Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance](https://mlsanity.com/paper/2508.06052/) (2025)

## Abstract (arXiv)

This work considers the optimal covariance steering problem for systems subject to both additive noise and uncertain parameters which may enter multiplicatively with the state and the control. The unknown parameters are modeled as a constant random variable sampled from a distribution with known moments. The optimal covariance steering problem is formulated using a moment-based representation of the system dynamics, which includes dependence between the unknown parameters and future states, and is solved using sequential convex programming. The proposed approach is demonstrated numerically using a holonomic spacecraft system and an autonomous vehicle control application.