# A machine learning based control of complex systems

## TL;DR

This paper presents a novel machine learning-based control method for complex systems, utilizing symbolic dynamics and homoclinic orbits to steer chaotic systems towards desired states, demonstrated through simulations.

## Contribution

It introduces a data-driven control strategy that does not rely on explicit system modeling but uses machine learning to approximate control signals based on current states.

## Key findings

- Effective control of chaotic systems demonstrated in simulations.
- Control strategy applicable to discrete maps, differential equations, and coupled networks.
- Shows potential of machine learning in complex system regulation.

## Abstract

In this work, inspired in the symbolic dynamic of chaotic systems and using machine learning techniques, a control strategy for complex systems is designed. Unlike the usual methodologies based on modeling, where the control signal is obtained from an approximation of the dynamic rule, here the strategy rest upon an approach of a function, that from the current state of the system, give the necessary perturbation to bring the system closer to a homoclinic orbit that naturally goes to the target. The proposed methodology is data-driven or can be developed in a based-model context and is illustrated with computer simulations of chaotic systems given by discrete maps, ordinary differential equations and coupled maps networks. Results shows the usefulness of the design of control techniques based on machine learning and numerical approach of homoclinic orbits.