  In this paper, we propose a deep unfolding-based framework for the output
feedback control of systems with input saturation. Although saturation commonly
arises in several practical control systems, there is still a scarce of
effective design methodologies that can directly deal with the severe
non-linearity of the saturation operator. In this paper, we aim to design an
anti-windup controller for enlarging the region of stability of the closed-loop
system by learning from the numerical simulations of the closed-loop system.
The data-driven framework we propose in this paper is based on a deep-learning
technique called Neural Ordinary Differential Equations. Within our framework,
we first obtain a candidate controller by using the deep-learning technique,
which is then tested by the existing theoretical results already established in
the literature, thereby avoiding the computational challenge in the
conventional design methodologies as well as theoretically guaranteeing the
performance of the system. Our numerical simulation shows that the proposed
framework can significantly outperform a conventional design methodology based
on linear matrix inequalities.
