Suppressing unknown disturbances to dynamical systems using machine learning

TL;DR

This paper introduces a model-free machine learning approach to identify and suppress unknown disturbances in dynamical systems, demonstrated on chaotic circuits and systems with stochastic disturbances.

Contribution

A novel, model-free machine learning method for robustly identifying and suppressing a wide range of unknown disturbances in dynamical systems.

Findings

Successfully identified and suppressed deterministic disturbances in chaotic circuits.

Effectively handled stochastic disturbances in numerical simulations.

Method demonstrated robustness under mild training restrictions.

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.