Suppressing unknown disturbances to dynamical systems using machine learning
Abstract
Identifying and suppressing unknown disturbances to dynamical systems is a problem with applications in many different fields. Here we present a model-free method to identify and suppress an unknown disturbance to an unknown system based only on previous observations of the system under the influence of a known forcing function. We find that, under very mild restrictions on the training function, our method is able to robustly identify and suppress a large class of unknown disturbances. We illustrate our scheme with the identification of both deterministic and stochastic unknown disturbances to an analog electric chaotic circuit and with numerical examples where a chaotic disturbance to various chaotic dynamical systems is identified and suppressed.
Keywords
Cite
@article{arxiv.2307.03690,
title = {Suppressing unknown disturbances to dynamical systems using machine learning},
author = {Juan G. Restrepo and Clayton P. Byers and Per Sebastian Skardal},
journal= {arXiv preprint arXiv:2307.03690},
year = {2024}
}