# Computationally Efficient Covariance Steering for Systems Subject to Parametric Disturbances and Chance Constraints

arXiv: https://arxiv.org/abs/2301.07308 · HTML: https://mlsanity.com/paper/2301.07308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2023.

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

- [Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions](https://mlsanity.com/paper/2605.04246/) (2026)
- [Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance](https://mlsanity.com/paper/2508.06052/) (2025)

## Abstract (arXiv)

This work investigates the finite-horizon optimal covariance steering problem for discrete-time linear systems subject to both additive and multiplicative uncertainties as well as state and input chance constraints. In particular, a tractable convex approximation of the optimal covariance steering problem is developed by tightening the chance constraints and by introducing a suitable change of variables. The solution of the convex approximation is shown to be a valid (albeit potentially suboptimal) solution to the original chance-constrained covariance steering problem.