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.

Anesthesia is arguably one of the most significant milestones in medical history, fundamentally transforming surgery from a traumatic last resort into a safe, humane, and sophisticated science. Closed-loop anesthesia represents the cutting edge of surgical pharmacology, utilizing automated systems to maintain a patient’s state of sedation or analgesia. Designing and testing controllers can be easily facilitated using dedicated patient simulators. This review provides a critical assessment of current patient simulators, specifically examining their mathematical models, functional capabilities, and utility in control system engineering. Through a series of simulation examples and a comparative analysis, the paper illustrates the incremental value of each tool relative to earlier iterations. Additionally, the scope extends to web-based simulators and engines, culminating in a roadmap for future research and technological advancements in the field. By highlighting the advantages and limitations of existing patient simulators and providing insight into possible developments, this survey emphasizes the need for further and deeper research on this topic. Such improvements are vital for the possible acceptance of closed-loop anesthesia in clinical settings.

According to data from the World Federation of Societies of Anesthesiologists, numerous countries across Asia and Africa have fewer than one anaesthesiologist per 100,000 people. Upskilling nurse anaesthetists in these regions is critical to improving clinical outcomes, and interactive virtual patient simulators offer a safe environment to explore complex clinical scenarios. This paper introduces an advanced general anaesthesia patient simulator engineered to bridge the accessibility gap left by existing platforms, which often require expert programming knowledge and restrict users to manual titration. Our simulator features an intuitive graphical user interface optimised for clinical education and natively supports both manual and closed-loop anaesthesia administration. The platform includes a suite of pre-designed controllers, specifically standard PIDs and two distinct fractional-order FO-PID variants, highlighting a novel robust FO-PID framework engineered to mitigate high patient variability. The deployment of these embedded controllers is demonstrated via a Depth of Hypnosis regulation case study and validated across a diverse cohort of 19 virtual patients. Closed-loop evaluation reveals that while the standard PID achieves a lower average mean squared error during the maintenance phase, the fractional-order alternatives deliver significantly superior robustness and inter-patient consistency. Ultimately, integrating this simulator into clinical training frameworks offers a viable pathway to reduce nursing workload and enhance patient safety through optimised automated drug delivery.

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.