Optimizing Nonlinear Dynamical Systems through Adaptive Gray-Box Feedback Control

The core message of this article is to develop a gray-box feedback optimization controller that combines the complementary benefits of model-based and model-free approaches to efficiently optimize the steady-state operation of nonlinear dynamical systems. The proposed controller adaptively fuses approximate sensitivities and model-free gradient estimates to achieve a balanced closed-loop behavior, retaining provable sample efficiency and optimality guarantees for nonconvex problems.