Koopman operator dynamical models: Learning, analysis and control

Petar Bevanda, Stefan Sosnowski, Sandra Hirche

Research output: Contribution to journalReview articlepeer-review

42 Scopus citations


The Koopman operator allows for handling nonlinear systems through a globally linear representation. In general, the operator is infinite-dimensional – necessitating finite approximations – for which there is no overarching framework. Although there are principled ways of learning such finite approximations, they are in many instances overlooked in favor of, often ill-posed and unstructured methods. Also, Koopman operator theory has long-standing connections to known system-theoretic and dynamical system notions that are not universally recognized. Given the former and latter realities, this work aims to bridge the gap between various concepts regarding both theory and tractable realizations. Firstly, we review data-driven representations (both unstructured and structured) for Koopman operator dynamical models, categorizing various existing methodologies and highlighting their differences. Furthermore, we provide concise insight into the paradigm's relation to system-theoretic notions and analyze the prospect of using the paradigm for modeling control systems. Additionally, we outline the current challenges and comment on future perspectives.

Original languageEnglish
Pages (from-to)197-212
Number of pages16
JournalAnnual Reviews in Control
StatePublished - Jan 2021


  • Data-based control
  • Dynamical models
  • Koopman operator
  • Representation learning
  • System analysis


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