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• W3198294736 abstract "Abstract We propose and demonstrate a nonlinear control method that can be applied to unknown, complex systems where the controller is based on a type of artificial neural network known as a reservoir computer. In contrast to many modern neural-network-based control techniques, which are robust to system uncertainties but require a model nonetheless, our technique requires no prior knowledge of the system and is thus model-free. Further, our approach does not require an initial system identification step, resulting in a relatively simple and efficient learning process. Reservoir computers are well-suited to the control problem because they require small training data sets and remarkably low training times. By iteratively training and adding layers of reservoir computers to the controller, a precise and efficient control law is identified quickly. With examples on both numerical and high-speed experimental systems, we demonstrate that our approach is capable of controlling highly complex dynamical systems that display deterministic chaos to nontrivial target trajectories." @default.
• W3198294736 created "2021-09-13" @default.
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• W3198294736 date "2021-09-01" @default.
• W3198294736 modified "2023-10-18" @default.
• W3198294736 title "Model-free control of dynamical systems with deep reservoir computing" @default.
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• W3198294736 doi "https://doi.org/10.1088/2632-072x/ac24f3" @default.
• W3198294736 hasPublicationYear "2021" @default.
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