A scenario-driven framework for reproducible in silico testing of physiological closed-loop control systems

Abstract

Physiological closed-loop control systems (PCLCS) are safety critical and require rigorous evaluation. Yet, in silico testing is often limited by study-specific setups with restricted reproducibility, traceability, and reusability. This work presents a modular, scenario-driven framework for traceable and reproducible testing of PCLCS. The framework decouples models, controllers, and testing scenarios, enabling structured and modular composition of experiments from reusable components. Parameter sets can be derived from literature ranges or fitted from data to provide patient-specific models (digital twins). Experiments are encoded in a structured, version-controllable format for advanced traceability. We demonstrate the framework using a closedloop mechanical ventilation controller for neonates and a neonatal patient model. Two experiments are conducted: setpoint tracking with and without spontaneous breathing, and replication of a previous in vivo experiment using a subjectspecific digital twin. Results illustrate evaluation across physiological variations with fully reproducible execution. This approach provides a foundation for rigorous and transparent testing of PCLCS.