# Data-Driven LQR Control Design

arXiv: https://arxiv.org/abs/1807.03263 · HTML: https://mlsanity.com/paper/1807.03263/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Robust Data-Driven Predictive Control using Reachability Analysis](https://mlsanity.com/paper/2103.14110/) (2021)
- [Linear Dynamics meets Linear MDPs: Closed-Form Optimal Policies via Reinforcement Learning](https://mlsanity.com/paper/2508.17185/) (2025)
- [Data-Driven Adversarial Online Control for Unknown Linear Systems](https://mlsanity.com/paper/2308.08138/) (2023)
- [MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR](https://mlsanity.com/paper/2402.14483/) (2024)
- [Data-Driven Continuous-Time Linear Quadratic Regulator via Closed-Loop and Reinforcement Learning Parameterizations](https://mlsanity.com/paper/2604.27922/) (2026)

## Abstract (arXiv)

This paper presents a data-driven solution to the discrete-time infinite horizon LQR problem. The state feedback gain is computed directly from a batch of input and state data collected from the plant. Simulation examples illustrate the convergence of the proposed solution to the optimal LQR gain as the number of Markov parameters tends to infinity. Experiments in an uninterruptible power supply are presented, which demonstrate the practical applicability of the design methodology.