# Model-free control

arXiv: https://arxiv.org/abs/1305.7085 · HTML: https://mlsanity.com/paper/1305.7085/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2013.

## Built on by (most cited first, 7 of 7)

- [On the sample complexity of stabilizing linear dynamical systems from data](https://mlsanity.com/paper/2203.00474/) (2022)
- [Context-aware controller inference for stabilizing dynamical systems from scarce data](https://mlsanity.com/paper/2207.11049/) (2022)
- [System stabilization with policy optimization on unstable latent manifolds](https://mlsanity.com/paper/2407.06418/) (2024)
- [Model-Free Predictive Control: Introductory Algebraic Calculations, and a Comparison with HEOL and ANNs](https://mlsanity.com/paper/2502.00443/) (2025)
- [Position control of an acoustic cavitation bubble by reinforcement learning](https://mlsanity.com/paper/2312.05674/) (2023)
- [An adaptive data sampling strategy for stabilizing dynamical systems via controller inference](https://mlsanity.com/paper/2506.01816/) (2025)
- [Avoidance of an unexpected obstacle without reinforcement learning: Why not using advanced control-theoretic tools?](https://mlsanity.com/paper/2509.03721/) (2025)

## Abstract (arXiv)

"Model-free control" and the corresponding "intelligent" PID controllers (iPIDs), which already had many successful concrete applications, are presented here for the first time in an unified manner, where the new advances are taken into account. The basics of model-free control is now employing some old functional analysis and some elementary differential algebra. The estimation techniques become quite straightforward via a recent online parameter identification approach. The importance of iPIs and especially of iPs is deduced from the presence of friction. The strange industrial ubiquity of classic PID's and the great difficulty for tuning them in complex situations is deduced, via an elementary sampling, from their connections with iPIDs. Several numerical simulations are presented which include some infinite-dimensional systems. They demonstrate not only the power of our intelligent controllers but also the great simplicity for tuning them.