Fractional-order Model Predictive Control Design for Anesthesia–Hemodynamic stabilization During Surgical Procedures

Delivering safe, patient-specific anesthesia demands simultaneous regulation of general-anesthesia and hemodynamic. This study proposes a steady state decoupled Fractional-Order Model Predictive Control (FOMPC) design of hemodynamic system under general anesthesia conditions particularly for patients during surgical procedures. We used a patient numerical model to validate the introduced control algorithm with comparative tests against other controllers. The aim is to stabilize the patient’s mean arterial pressure (MAP) and cardiac output (CO), taking into account the subsystems coupling (synergic and antagonistic). Based on a fractional-order predictive model we show via intensive simulation experiments the robustness of the system against internal interactions and external disturbances and its efficiency to meet the clinical objectives.