Adaptive control in dynamical systems using reservoir computing
Abstract
We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to predict a system parameter from the time series data. Subsequently, a control signal based on the predicted parameter can be used as feedback to the dynamical system to lead it to a target state. Our results show that the dynamical system can be controlled throughout a wide range of attractor types. One set of training data consisting of only a few time series corresponding to the known parameter values enables our scheme to control a dynamical system to an arbitrary target attractor starting from any other initial attractor. In addition to numerical results, we implement our scheme in real-world systems like on a R\"{o}ssler system realized in an electronic circuit to demonstrate the effectiveness of our approach.
Keywords
Cite
@article{arxiv.2412.17501,
title = {Adaptive control in dynamical systems using reservoir computing},
author = {Swarnendu Mandal and Swati Chauhan and Umesh Kumar Verma and Manish Dev Shrimali and Kazuyuki Aihara},
journal= {arXiv preprint arXiv:2412.17501},
year = {2024}
}