AI-driven control of chaos: A transformer-based approach for dynamical systems

Abstract

Chaotic behavior in dynamical systems poses a significant challenge in trajectory control,traditionally relying on computationally intensive physical models. We present a machine learning-based algorithm to compute the minimum control bounds required to confine particles within a region indefinitely, using only the first iterations of diverging orbits as required information of the system. This model-free approach achieves high accuracy, with a mean squared error of 2.88×10−4 and computation times in the range of seconds. The results highlight its efficiency and potential for real-time control of chaotic systems