# Meta-learning of data-driven controllers with automatic model reference tuning: theory and experimental case study

arXiv: https://arxiv.org/abs/2403.14500 · HTML: https://mlsanity.com/paper/2403.14500/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Mar 2024.

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

- [Learning Nonlinear Systems In-Context: From Synthetic Data to Real-World Motor Control](https://mlsanity.com/paper/2602.07173/) (2026)

## Abstract (arXiv)

Data-driven control offers a viable option for control scenarios where constructing a system model is expensive or time-consuming. Nonetheless, many of these algorithms are not entirely automated, often necessitating the adjustment of multiple hyperparameters through cumbersome trial-and-error processes and demanding significant amounts of data. In this paper, we explore a meta-learning approach to leverage potentially existing prior knowledge about analogous (though not identical) systems, aiming to reduce both the experimental workload and ease the tuning of the available degrees of freedom. We validate this methodology through an experimental case study involving the tuning of proportional, integral (PI) controllers for brushless DC (BLDC) motors with variable loads and architectures.