Koopman operator theory: fundamentals, control, and applications
The Koopman operator has gained considerable attention due to its ability to provide a global linear representation of highly complex dynamical systems. The operator describes nonlinear dynamics in a linear way through the lens of real- or complex-valued observable functions. Data-driven techniques, like extended dynamic mode decomposition (EDMD), kernel EDMD, and machine-learning methods, can be used to generate finite-dimensional approximations accompanied by finite-data error bounds. In this tutorial paper, we provide a concise introduction into Koopman operator theory and its use in systems and control. A particular focus is put on data-driven surrogate models, their extension to syst...
| Authors | Igor Mezić, Jorge Cortés, Karl Worthmann et al. |
|---|---|
| Primary category | eess.SY |
| Published | 2 July 2026 |
| Last updated at source | 8 October 2026 |
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