A Closed-loop Framework to Discriminate Models Using Optimal Control

This paper introduces a closed-loop framework for discriminating between different dynamical system models by leveraging optimal control. The approach aims to design optimal inputs that maximize the divergence of system outputs, thereby enhancing the ability to distinguish between competing models. This has significant implications for system identification, fault detection, and personalized medicine, where accurate model discrimination is crucial for effective decision-making and intervention.