Controlling Dynamical Systems into Unseen Target States Using Machine Learning

TL;DR

This paper introduces a data-driven, model-free machine learning approach using reservoir computing to control complex dynamical systems into unseen and complex target states, including chaotic regimes, with high efficiency and robustness.

Contribution

The paper presents a novel parameter-aware reservoir computing method for controlling dynamical systems into previously unobserved and complex target states, including chaotic dynamics.

Findings

Successfully controls systems into unseen chaotic states

Achieves fast transitions with minimal transient behavior

Demonstrates effectiveness on a nonlinear power system model

Abstract

We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of next-generation reservoir computing (NGRC), our approach accurately predicts system behavior in unobserved parameter regimes, enabling control over transitions to arbitrary target states utilizing a new prediction evaluation and selection scheme. Crucially, this includes states with dynamics that differ fundamentally from known regimes, such as shifts from periodic to intermittent or chaotic behavior. The method's parameter awareness facilitates non-stationary control with which control scenarios are generated and evaluated on the basis of predefined control objective. In addition to proving the method for transient-free control to extrapolated chaotic target states over transition times, we demonstrate the method's effectiveness on a nonlinear power system model. Our method successfully navigates transitions even in scenarios where system collapse is observed frequently, while ensuring fast transitions and avoiding prolonged transient behavior. By extending the applicability of machine learning-based control mechanisms to previously inaccessible target dynamics, the methodology opens the door to new control applications while maintaining exceptional efficiency.