  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 a numerical example where a chaotic disturbance to the
Lorenz system is identified and suppressed.
