# Online convex optimization for robust control of constrained dynamical systems

arXiv: https://arxiv.org/abs/2401.04487 · HTML: https://mlsanity.com/paper/2401.04487/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2024.

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

- [Online Learning of Kalman Filtering: From Output to State Estimation](https://mlsanity.com/paper/2603.27159/) (2026)

## Abstract (arXiv)

This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances. We combine the online convex optimization framework with tools from robust model predictive control to propose an algorithm that is able to guarantee robust constraint satisfaction. The performance of the closed loop emerging from application of our framework is studied in terms of its dynamic regret, which is proven to be bounded linearly by the variation of the cost functions and the magnitude of the disturbances. We corroborate our theoretical findings and illustrate implementational aspects of the proposed algorithm by a numerical case study on a tracking control problem of an autonomous vehicle.