---
_id: '21199'
abstract:
- lang: eng
  text: "As in almost every other branch of science, the major advances in data\r\nscience
    and machine learning have also resulted in significant improvements\r\nregarding
    the modeling and simulation of nonlinear dynamical systems. It is\r\nnowadays
    possible to make accurate medium to long-term predictions of highly\r\ncomplex
    systems such as the weather, the dynamics within a nuclear fusion\r\nreactor,
    of disease models or the stock market in a very efficient manner. In\r\nmany cases,
    predictive methods are advertised to ultimately be useful for\r\ncontrol, as the
    control of high-dimensional nonlinear systems is an engineering\r\ngrand challenge
    with huge potential in areas such as clean and efficient energy\r\nproduction,
    or the development of advanced medical devices. However, the\r\nquestion of how
    to use a predictive model for control is often left unanswered\r\ndue to the associated
    challenges, namely a significantly higher system\r\ncomplexity, the requirement
    of much larger data sets and an increased and often\r\nproblem-specific modeling
    effort. To solve these issues, we present a universal\r\nframework (which we call
    QuaSiModO:\r\nQuantization-Simulation-Modeling-Optimization) to transform arbitrary\r\npredictive
    models into control systems and use them for feedback control. The\r\nadvantages
    of our approach are a linear increase in data requirements with\r\nrespect to
    the control dimension, performance guarantees that rely exclusively\r\non the
    accuracy of the predictive model, and only little prior knowledge\r\nrequirements
    in control theory to solve complex control problems. In particular\r\nthe latter
    point is of key importance to enable a large number of researchers\r\nand practitioners
    to exploit the ever increasing capabilities of predictive\r\nmodels for control
    in a straight-forward and systematic fashion."
article_number: '110840'
author:
- first_name: Sebastian
  full_name: Peitz, Sebastian
  id: '47427'
  last_name: Peitz
  orcid: 0000-0002-3389-793X
- first_name: Katharina
  full_name: Bieker, Katharina
  id: '32829'
  last_name: Bieker
citation:
  ama: Peitz S, Bieker K. On the Universal Transformation of Data-Driven Models to
    Control Systems. <i>Automatica</i>. 2023;149. doi:<a href="https://doi.org/10.1016/j.automatica.2022.110840">10.1016/j.automatica.2022.110840</a>
  apa: Peitz, S., &#38; Bieker, K. (2023). On the Universal Transformation of Data-Driven
    Models to Control Systems. <i>Automatica</i>, <i>149</i>, Article 110840. <a href="https://doi.org/10.1016/j.automatica.2022.110840">https://doi.org/10.1016/j.automatica.2022.110840</a>
  bibtex: '@article{Peitz_Bieker_2023, title={On the Universal Transformation of Data-Driven
    Models to Control Systems}, volume={149}, DOI={<a href="https://doi.org/10.1016/j.automatica.2022.110840">10.1016/j.automatica.2022.110840</a>},
    number={110840}, journal={Automatica}, publisher={Elsevier}, author={Peitz, Sebastian
    and Bieker, Katharina}, year={2023} }'
  chicago: Peitz, Sebastian, and Katharina Bieker. “On the Universal Transformation
    of Data-Driven Models to Control Systems.” <i>Automatica</i> 149 (2023). <a href="https://doi.org/10.1016/j.automatica.2022.110840">https://doi.org/10.1016/j.automatica.2022.110840</a>.
  ieee: 'S. Peitz and K. Bieker, “On the Universal Transformation of Data-Driven Models
    to Control Systems,” <i>Automatica</i>, vol. 149, Art. no. 110840, 2023, doi:
    <a href="https://doi.org/10.1016/j.automatica.2022.110840">10.1016/j.automatica.2022.110840</a>.'
  mla: Peitz, Sebastian, and Katharina Bieker. “On the Universal Transformation of
    Data-Driven Models to Control Systems.” <i>Automatica</i>, vol. 149, 110840, Elsevier,
    2023, doi:<a href="https://doi.org/10.1016/j.automatica.2022.110840">10.1016/j.automatica.2022.110840</a>.
  short: S. Peitz, K. Bieker, Automatica 149 (2023).
date_created: 2021-02-10T07:04:15Z
date_updated: 2023-01-07T12:01:58Z
department:
- _id: '101'
- _id: '655'
doi: 10.1016/j.automatica.2022.110840
intvolume: '       149'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://www.sciencedirect.com/science/article/pii/S0005109822007075/pdfft?isDTMRedir=true&download=true
oa: '1'
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Automatica
publication_status: published
publisher: Elsevier
status: public
title: On the Universal Transformation of Data-Driven Models to Control Systems
type: journal_article
user_id: '47427'
volume: 149
year: '2023'
...