Anesthesia implies maintaining a triad of hypnosis, analgesia, and neuromuscular blockade by infusing several drugs which specifically act on each of the above aspects. This work focuses on controlling the hypnosis by automatic infusion of propofol using bispectral index (BIS) as the primary controlled variable. A fourthorder nonlinear pharmacokinetic (PK)-pharmacodynamic (PD) representation is used for the hypnosis dynamics of patients. A reliable PK-PD model with associated parameters is obtained from the literature and the closed-loop responses of four types of control strategies (model predictive control, internal model control, controller with modeling error compensation, and proportional-integral-derivative (PID) control) are compared. Robust performance of the four controllers is tested for a broad range of patients by considering variability in PK-PD parameters. Also, the relative performance of the four controllers is studied for different setpoints, noise, and disturbances in BIS signal. Numerical simulations show that all the advanced controllers performed better than the PID controller with the model predictive controller showing the best performance.
Although manual control of anesthesia is still the dominant practice during surgery, an increasing number of studies have been conducted to investigate the possibility of automating this procedure. Several clinical studies that compare closed-loop to manual anesthesia control performance have been reported. These studies used proportional-integral-derivative (PID) controllers, as well as model-based controllers. However, there is a need for a comprehensive evaluation of closed-loop systems, to establish their safety, reliability, and efficacy for anesthesia regulation. This requires a detailed evaluation of promising and/or recent controllers for a range of patients and conditions via simulation. The present study investigates the performance of singleloop PID, cascade, model-predictive, and RTDA (Robustness, set point Tracking, Disturbance rejection, Aggressiveness) controllers for closed-loop regulation of hypnosis using isoflurane with bispectral index (BIS) as the controlled variable. Extensive simulations are performed using a model that simulates patient responses to the drug, surgical stimuli, and unexpected surgical events. Results of this comprehensive evaluation show that model-predictive and RTDA controllers provide better regulation of BIS, compared to the other controllers tested.
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