# Data informativity: a new perspective on data-driven analysis and control

arXiv: https://arxiv.org/abs/1908.00468 · HTML: https://mlsanity.com/paper/1908.00468/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [Data-Driven Reachability Analysis from Noisy Data](https://mlsanity.com/paper/2105.07229/) (2021)
- [On the sample complexity of stabilizing linear dynamical systems from data](https://mlsanity.com/paper/2203.00474/) (2022)
- [Robust Data-Driven Predictive Control using Reachability Analysis](https://mlsanity.com/paper/2103.14110/) (2021)
- [Context-aware controller inference for stabilizing dynamical systems from scarce data](https://mlsanity.com/paper/2207.11049/) (2022)
- [An Efficient Off-Policy Reinforcement Learning Algorithm for the Continuous-Time LQR Problem](https://mlsanity.com/paper/2303.17819/) (2023)
- [Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information](https://mlsanity.com/paper/2106.10533/) (2021)
- [On the Optimization Landscape of Dynamic Output Feedback: A Case Study for Linear Quadratic Regulator](https://mlsanity.com/paper/2209.05042/) (2022)
- [Stability-Certified On-Policy Data-Driven LQR via Recursive Learning and Policy Gradient](https://mlsanity.com/paper/2403.05367/) (2024)
- [Learning Noise-Robust Stable Koopman Operator for Control with Hankel DMD](https://mlsanity.com/paper/2408.06607/) (2024)
- [Discrete fully probabilistic design: towards a control pipeline for the synthesis of policies from examples](https://mlsanity.com/paper/2112.11210/) (2021)
- [LMI-based Data-Driven Robust Model Predictive Control](https://mlsanity.com/paper/2303.04777/) (2023)
- [Interpolation Conditions for Data Consistency and Prediction in Noisy Linear Systems](https://mlsanity.com/paper/2504.08484/) (2025)
- [Direct Data Driven Control Using Noisy Measurements](https://mlsanity.com/paper/2505.06407/) (2025)
- [Fine-tuning for Data-enabled Predictive Control of Noisy Systems by Reinforcement Learning](https://mlsanity.com/paper/2505.24572/) (2025)
- [Kernel-Based Sparse Additive Nonlinear Model Structure Detection through a Linearization Approach](https://mlsanity.com/paper/2508.01453/) (2025)
- [Adaptive Control of Unknown Linear Switched Systems via Policy Gradient Methods](https://mlsanity.com/paper/2601.03016/) (2026)
- [Data-Driven Continuous-Time Linear Quadratic Regulator via Closed-Loop and Reinforcement Learning Parameterizations](https://mlsanity.com/paper/2604.27922/) (2026)
- [When Can Safe Controllers Adapt? Information before Commitment](https://mlsanity.com/paper/2607.16895/) (2026)
- [(Cheap) Stochastic Policy Gradient Converges with High Probability for Linear Quadratic Regulator](https://mlsanity.com/paper/2609.19712/) (2026)

## Abstract (arXiv)

The use of persistently exciting data has recently been popularized in the context of data-driven analysis and control. Such data have been used to assess system theoretic properties and to construct control laws, without using a system model. Persistency of excitation is a strong condition that also allows unique identification of the underlying dynamical system from the data within a given model class. In this paper, we develop a new framework in order to work with data that are not necessarily persistently exciting. Within this framework, we investigate necessary and sufficient conditions on the informativity of data for several data-driven analysis and control problems. For certain analysis and design problems, our results reveal that persistency of excitation is not necessary. In fact, in these cases data-driven analysis/control is possible while the combination of (unique) system identification and model-based control is not. For certain other control problems, our results justify the use of persistently exciting data as data-driven control is possible only with data that are informative for system identification.