Model-Free Control of Dynamical Systems with Deep Reservoir Computing

TL;DR

This paper introduces a model-free control method using reservoir computing that effectively manages complex, unknown dynamical systems without prior system knowledge or extensive training, demonstrated on chaotic and experimental systems.

Contribution

The paper presents a novel reservoir computing-based control approach that is model-free, requires minimal training data, and can control complex systems efficiently without initial system identification.

Findings

Successfully controls chaotic systems

Requires minimal training data and time

Effective on both numerical and experimental systems

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.