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.

翻译：识别并抑制动力系统中的未知扰动是一个在多个领域具有应用价值的问题。本文提出一种无需模型的方法，仅基于系统在已知驱动力作用下的历史观测数据，即可识别并抑制作用于未知系统的未知扰动。研究发现，在训练函数满足极宽松约束的条件下，该方法能够稳健地识别并抑制一大类未知扰动。我们通过模拟电路混沌系统中确定性及随机性未知扰动的识别案例，以及洛伦兹系统中混沌扰动被识别并抑制的数值算例，对本方案进行了演示。