Taming nonlinear dynamics with MLC

Abstract

We investigate the application of machine learning control (MLC) to the stabilization of a nonlinear dynamical system. This plant features a frequently observed frequency crosstalk between actuation and unstable dynamics. MLC explores and exploits this frequency crosstalk as an enabling actuation mechanism. MLC-based feedback is benchmarked against open- and closed-loop forcing derived from Kryloff-Bogoliubov approximation.