{"author":[{"last_name":"Peitz","full_name":"Peitz, Sebastian","first_name":"Sebastian","orcid":"0000-0002-3389-793X","id":"47427"},{"first_name":"Katharina","id":"32829","full_name":"Bieker, Katharina","last_name":"Bieker"}],"status":"public","intvolume":"       149","_id":"21199","year":"2023","type":"journal_article","publication":"Automatica","date_created":"2021-02-10T07:04:15Z","oa":"1","volume":149,"doi":"10.1016/j.automatica.2022.110840","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."}],"publication_status":"published","date_updated":"2023-01-07T12:01:58Z","language":[{"iso":"eng"}],"article_number":"110840","title":"On the Universal Transformation of Data-Driven Models to Control Systems","department":[{"_id":"101"},{"_id":"655"}],"project":[{"_id":"52","name":"PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"publisher":"Elsevier","citation":{"short":"S. Peitz, K. Bieker, Automatica 149 (2023).","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>.","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>.","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} }","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>","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>.","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>"},"user_id":"47427","main_file_link":[{"url":"https://www.sciencedirect.com/science/article/pii/S0005109822007075/pdfft?isDTMRedir=true&download=true","open_access":"1"}]}