Dictionary-Free Koopman Model Predictive Control with Nonlinear Input Transformation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00388361" target="_blank" >RIV/68407700:21230/25:00388361 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1137/23M1597174" target="_blank" >https://doi.org/10.1137/23M1597174</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1137/23M1597174" target="_blank" >10.1137/23M1597174</a>

Result language
angličtina
Original language name
Dictionary-Free Koopman Model Predictive Control with Nonlinear Input Transformation
Original language description
This paper introduces a method for data-driven control based on the Koopman operator model predictive control. Unlike existing approaches, the method does not require a dictionary and incorporates a nonlinear input transformation, thereby allowing for more accurate predictions with less ad hoc tuning. In addition to this, the method allows for input quantization and exploits symmetries, thereby reducing computational cost, both offline and online. Importantly, the method retains the convexity of the optimization problem solved within the model predictive control online. Numerical examples demonstrate superior performance compared to existing methods as well as the capacity to learn discontinuous lifting functions.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
20205 - Automation and control systems

Project
<a href="/en/project/GJ20-11626Y" target="_blank" >GJ20-11626Y: Koopman operator framework for control of complex nonlinear dynamical systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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