Alin-Ciprian Malița

Hemodynamic management is extremely important in cardiac patients undergoing surgery. Traditionally, the approach towards hemodynamic stabilization included the control of both mean arterial pressure (MAP) and cardiac output (CO) using Sodium Nitroprusside and Dopamine. More efficient and safer drugs have been introduced, such as Norepinephrine. The focus of this manuscript is to provide some preliminary results regarding the closed loop control of MAP using Norepinephrine. However, to design a dedicated control system, a mathematical model describing the effect of Norepinephrine on mean arterial pressure is required. Only a handful of papers describe a pharmacokinetic–pharmacodynamic (PK-PD) model. In this paper, a simplified model suitable for designing a controller is determined based on PK-PD insights and existing clinical data. Existing closed loop controllers are based on the simple proportional integral derivative (PID) controller, with limited robustness to patient variability. In this paper, two advanced control strategies are proposed to replace PID. The closed loop simulation results include reference tracking and disturbance rejection and show the efficiency and robustness of the proposed control algorithms. The preliminary results set the background for further research in this area.

In anesthesia, even minor miscalculations can pose significant risks to patient safety, underscoring the need for precise and resilient control systems. This paper explores the optimisation of Fractional-Order Proportional-Integral-Derivative controllers using Genetic Algorithms for Bispectral Index regulation during general anesthesia. The proposed approach fine-tunes controller parameters by leveraging a model closed-loop response, enabling the algorithm to handle both transient and steady-state aspects of the response. By incorporating a fitness function that evaluates key performance metrics, including steady-state error, overshoot, and oscillatory behaviour, the genetic algorithm efficiently explores the search space to identify optimal controller configurations. The optimised controller demonstrates the ability to regulate Bispectral Index levels while maintaining stability and robustness against disturbances.

Human error remains a significant concern in the medical field, particularly in anaesthesia, where even minor miscalculations can jeopardise patient safety. To address these challenges, the integration of automated control systems has emerged as a viable solution. Most existing control algorithms are tuned using a nominal patient model and inter-patient variability is tackled by incorporating robustness in the controller design. A personalised approach is, however, desirable. In this paper, a hybrid control framework that combines fractional-order control with a model-reference adaptive control (MRAC) approach is proposed as a solution for personalised control of the bispectral index (BIS). The system is designed to meet stringent performance requirements while ensuring stability and robustness. Comparative result with a non-adaptive fractional order controller are presented to demonstrate the efficiency of the proposed adaptive strategy. Simulation results demonstrate promising outcomes, both with respect to the selected criteria and in alignment with the anticipated future developments.