Model-free control of dynamical systems with deep reservoir computing

Abstract

A technique is provided for control of a nonlinear dynamical system to an arbitrary trajectory. The technique does not require any knowledge of the dynamical system, and thus is completely model-free. When applied to a chaotic system, it is capable of stabilizing unstable periodic orbits (UPOs) and unstable steady states (USSs), controlling orbits that require non-vanishing control signal, synchronization to other chaotic systems, and so on. It is based on a type of recurrent neural network (RNN) known as a reservoir computer (RC), which, as shown, is capable of directly learning how to control an unknown system. Precise control to a desired trajectory is obtained by iteratively adding layers to the controller, forming a deep recurrent neural network.

Dependencies are inferred from the claim text.