Suppressing unknown disturbances to dynamical systems using machine learning
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 44 references that are in the map. First posted on arXiv 19 Jun 2023.
Builds on
6 of 44 references.
References · 6
- 2022Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systemscited by 33
- 2021Parallel Machine Learning for Forecasting the Dynamics of Complex Networkscited by 17
- 2020Model-Free Control of Dynamical Systems with Deep Reservoir Computingcited by 12
- 2019Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Linkscited by 10
- 2018Data-driven Localization and Estimation of Disturbance in the Interconnected Power Systemcited by 2
- 2023Learning unidirectional coupling using echo-state networkcited by 2
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Citing papers per year: 2023: 1.
1 paper.