Suppressing unknown disturbances to dynamical systems using machine learning
This work addresses a critical issue in control theory with applications across various fields, but it appears incremental as it builds on existing model-free approaches for disturbance suppression.
The authors tackled the problem of identifying and suppressing unknown disturbances in dynamical systems by developing a model-free method that uses only previous observations under known forcing. They demonstrated its effectiveness by robustly identifying and suppressing a large class of disturbances, including deterministic and stochastic examples in an analog electric chaotic circuit and numerical simulations of chaotic systems.
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.