Evaluation of Fluid Resuscitation Control Algorithms via a Hardware-in-the-Loop Test Bed.

Area of Science:

• Physiology
• Control Systems Engineering
• Medical Device Testing

Background:

• Evaluating fluid resuscitation control algorithms is crucial for patient care.
• Existing testing methods like in silico and in vivo studies have limitations.
• A need exists for a testing platform that integrates physical components with computational models.

Purpose of the Study:

• To present a novel hardware-in-the-loop (HIL) testing platform for fluid resuscitation control algorithms.
• To validate the HIL platform's ability to accurately predict hemodynamic variables.
• To compare the performance of rule-based and PID fluid resuscitation controllers.

Main Methods:

• Developed a cyber-physical HIL testing platform integrating physical devices, computational models, and algorithms.
• Validated the HIL test bed against in silico and in vivo data for hemodynamic variable prediction.
• Investigated two fluid resuscitation control algorithms: decision table (rule-based) and proportional-integral-derivative (PID).

Main Results:

• The HIL test bed demonstrated statistically similar results to in silico and in vivo data, confirming its predictive accuracy for hemodynamic responses.
• Both rule-based and PID controllers effectively stabilized hemodynamic variables and achieved target endpoints at similar speeds.
• The PID controller exhibited higher accuracy compared to the rule-based controller in the tested scenarios.

Conclusions:

• The HIL testing platform is a viable tool for realistic evaluation of physiologic controllers.
• This platform facilitates rapid assessment of closed-loop control systems, supporting iterative design.
• HIL testing offers a complementary approach to existing methods for evaluating control systems under various conditions.