Can I read A Data-Driven Koopman Model Predictive Control Framework for Nonlinear Partial Differential Equations on EtoBox?

A Data-Driven Koopman Model Predictive Control Framework for Nonlinear Partial Differential Equations by Hassan Arbabi; Milan Korda; Igor Mezic is a scholarly article available to read on EtoBox.

What is A Data-Driven Koopman Model Predictive Control Framework for Nonlinear Partial Differential Equations about?

The Koopman operator theory is an increasingly popular formalism of dynamical systems theory which enables analysis and prediction of the state space dynamics from measurement data. Building on the recent development of the Koopman model predictive control framework [14], we propose a methodology for closed-loop feedback control of nonlinear partial differential equations in a fully data-driven and model-free manner. In the first step, we compute a Koopman-linear representation of the system using a variation of the extended dynamic mode decomposition algorithm and then we apply the model predictive control to the constructed linear model. Our methodology is also capable of utilizing sparse measurements by incorporating delayembedding of the available data into the identification and control processes. We illustrate the application of this methodology for the periodic Burgers' equation and the boundary control of fluid flow governed by the Navier-Stokes equations in a twodimensional cavity.

Author

Hassan Arbabi; Milan Korda; Igor Mezic

Publisher

IEEE

Published

2018

Language

EN