Learning neural controllers for nonlinear systems from data
The article proposes a data-driven method to design neural feedback controllers and Lyapunov functions for unknown nonlinear systems. It uses offline data to identify system dynamics, then jointly synthesizes these components while enforcing input constraints and verifying stability formally using SMT solvers. This provides a deterministic guarantee of stability even when the identification data is slightly perturbed, successfully stabilizing chaotic systems like the Lorenz model.