  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.
