# Safe data-driven reference tracking with prescribed performance

arXiv: https://arxiv.org/abs/2312.08734 · HTML: https://mlsanity.com/paper/2312.08734/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2023.

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

- [Reinforcement Learning for Docking Maneuvers with Prescribed Performance](https://mlsanity.com/paper/2402.08306/) (2024)

## Abstract (arXiv)

We study output reference tracking for unknown continuous-time systems with arbitrary relative degree. The control objective is to keep the tracking error within predefined time-varying bounds while measurement data is only available at discrete sampling times. To achieve the control objective, we propose a two-component controller. One part is a recently developed sampled-data zero-order hold controller, which achieves reference tracking within prescribed error bounds. To further improve the control signal, we explore the system dynamics via input-output data, and include as the second component a data-driven MPC scheme based on Willems et al.'s fundamental lemma. This combination yields significantly improved input signals as illustrated by a numerical example.