Suppressing unknown disturbances to dynamical systems using machine learning

Juan G. Restrepo

doi:10.1038/s42005-024-01885-2

Suppressing unknown disturbances to dynamical systems using machine learning

2024-12-19

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Per Sebastian Skardal *

DOI：10.1038/s42005-024-01885-2

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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.

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Suppressing unknown disturbances to dynamical systems using machine learning

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