Hyperparameter Selection in Deep Unfolding-based Static Output Feedback Stabilization

Abstract

The static output feedback stabilization problem has a basic and simple structure. However, this problem is NP-hard and difficult to solve. In this study, we apply the method of deep unfolding to design a static output feedback control system. We then investigate how the performance of algorithms based on deep unfolding for static output feedback stabilization depends on the hyperparameters of the optimizer. In particular, we provide a policy for choosing appropriate hyperparameters by evaluating the algorithms' stabilization success rates and learning times for various optimizers, learning rates, loss functions, and discretization periods.