Multivariable Control in General Anesthesia: A Comparative Study of Model Predictive and Fractional-Order Strategies for Effect-Site Concentration and Hemodynamic Regulation

Managing anesthesia and hemodynamic regulation during surgical procedures poses significant challenges due to patient-specific variability and the complex interactions of administered drugs. While advanced monitoring systems and Target-Controlled Infusion (TCI) devices enhance precision in drug delivery, their reliance on manual adjustments limits real-time adaptability. This study investigates two advanced control strategies–centralized Model Predictive Control (MPC) and decentralized fractional-order control–within a multivariable framework designed to regulate hypnosis, analgesia, and hemodynamic stabilization. Virtual patient simulations were used to evaluate these approaches under diverse anesthetic and, notably, hemodynamic conditions. The findings reveal that MPC delivers superior precision in maintaining the Bispectral Index (BIS) but tends to apply more aggressive adjustments to hemodynamic variables. In contrast, fractional-order control ensures smoother responses, though its ability to handle disturbances is less robust in certain scenarios. This study highlights the trade-offs between these strategies and advocates for further exploration of hybrid solutions to enhance patient safety and surgical outcomes.