Adaptive control in dynamical systems using reservoir computing

TL;DR

This paper introduces a reservoir computing-based data-driven method for adaptive control of dynamical systems, capable of predicting parameters and guiding systems to desired states across various attractors, demonstrated both numerically and experimentally.

Contribution

It presents a novel reservoir computing approach for adaptive control that requires minimal training data and applies to real-world systems.

Findings

Effective control across diverse attractor types

Successful parameter prediction from limited data

Implementation on electronic circuits confirms practicality

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.