Reduced-order control using low-rank dynamic mode decomposition

Low-rank Approximation and Dynamic Mode Decomposition

Dynamic Mode Decomposition (DMD) has emerged as a powerful tool for analyzing the dynamics of non-linear systems from experimental datasets. Recently, several attempts have extended DMD to the context of low-rank approximations. This low-rank extension takes the form of a nonconvex optimisation problem. To the best of our knowledge, only sub-optimal algorithms have been proposed in the literatu...

Randomized low-rank Dynamic Mode Decomposition for motion detection

This paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but is also suitable for motion detection in video streams and its use for backg...

Higher Order Dynamic Mode Decomposition

On Reduced Input-Output Dynamic Mode Decomposition

The identification of reduced-order models from high-dimensional data is a challenging task, and even more so if the identified system should not only be suitable for a certain data set, but generally approximate the input-output behavior of the data source. In this work, we consider the input-output dynamic mode decomposition method for system identification. We compare excitation approaches f...

Wind Farm Modeling and Control Using Dynamic Mode Decomposition

The objective of this paper is to construct a low-order model of a wind farm that can be used for control design and analysis. There is a potential to use wind farm control to increase power and reduce overall structural loads by properly coordinating the turbines in a wind farm. To perform control design and analysis, a model of the wind farm needs to be constructed that has low computational ...