milankorda / koopmanmpc_for_flowcontrol

A data-driven framework for control of nonlinear flows with Koopman Model Predictive Control

KoopmanMPC_for_flowcontrol

This project demonstates the application of Koopman-MPC framework for flow control with the example of Burgers equation, following the paper "A data-driven Koopman model predictive control framework for nonlinear flows" by H. Arbabi, M. Korda and I. Mezic.

The Koopman-MPC framework is summarized in the below figure:

files in the root folder:

BurgersExample: Runs the Burgers example as explained in the paper, it includes data collection, Extended Dynamic Mode Decomposition (EDMD) for identification of the Koopman linear system, and a run of closed-loop controlled system from some initial condition. Feel free to play with the paremeters of the code, in particular, try different observables, embedding dimension, reference signal, initial condition, etc. The whole program, with the initial paremeter settings, runs on my personal laptop in under 2 minutes.

before you run the code:

go to "./thehood/" and unzip "qpOASES-3.1.0" go to subfolder ".\thehood\qpOASES-3.1.0\interfaces\matlab" and run make.m . This is required to activate the qpOASIS interface for solving the optimization problem.

send comments and questions to

[email protected]

H Arbabi

April 2018