# Data-driven reference model selection and application to L-DDC design

## TL;DR

This paper introduces a data-driven method for selecting reference models in control design, ensuring they are both achievable by the plant and meet desired performance criteria, specifically for LTI systems.

## Contribution

It proposes a novel approach to build reproducible and performance-oriented reference models using plant instability estimation and data-driven stability analysis.

## Key findings

- Improved reference model selection enhances control performance.
- The method enables validation via small-gain theorem.
- Application to L-DDC demonstrates practical effectiveness.

## Abstract

The choice of a reference model in data-driven control techniques is a critical step. Indeed, it should represent the desired closed-loop performances and be achievable by the plant at the same time. In this paper, we propose a method to build such a reference model, both reproducible by the system and having a desired behaviour. It is applicable to Linear Time-Invariant (LTI) monovariable systems and relies on the estimation of the plant's instabilities through a data-driven stability analysis technique. The L-DDC (Loewner Data Driven Control) algorithm is used to illustrate the impact of the choice of the reference model on the control design process. Finally, the proposed choice of specifications allows to use a controller validation technique based on the small-gain theorem.