  We propose a deep unfolding-based approach for stabilization of time-delay
linear systems. Deep unfolding is an emerging framework for design and
improvement of iterative algorithms and attracting significant attentions in
signal processing. In this paper, we propose an algorithm to design a static
output feedback gain for stabilizing time-delay linear systems via deep
unfolding. Within the algorithm, the learning part is driven by NeuralODE
developed in the community of machine learning, while the gain verification is
performed with linear matrix inequalities developed in the systems and control
theory. The effectiveness of the proposed algorithm is illustrated with
numerical simulations.
