Controlling depth of anesthesia using PID tuning: a comparative model-based study

Abstract

The control of depth of anesthesia presents a challenging and realistic problem that calls for fast and adaptable control techniques. However, patient-to-patient variability in model parameters poses the question of which control strategy can generate best results. In this paper, we studied three different patient models, i.e., the slate model, the isoflurane unconsciousness model, and the isoflurane to muscle relaxation interaction model. Due to the simplicity of the PID control and easy implementation, a discrete PID controller with actuator saturation is considered in this study to supports these three pharmacokinetic models. The models being tested can give us an understanding of the effects of disposition of drug in the body by measuring the Mean Arterial Pressure (MAP). The system characteristics are such that the PID controller should regulate the error signal of the gases sodium nitroprusside (SNP) and isoflurane using MAP readings from the patient. The system responses under the discrete PID control are presented and the results show acceptable performance.

Increasing Robustness of the Anesthesia Process from Difference Patient's Delay Using a State-Space Model Predictive Controller

“…Recently, several methods have been introduced for controlling the Depth of Anesthesia (DOA) [3]. Fixed gain controllers such as P, PI, and PID strategies can perform well when used in clinical therapy and under certain conditions [4,5], on the other hand, It can lead poor performances because of the large variability between subjects and the delay which exists in the patient's model. The process of anesthesia is nonlinear with time delay and there are also some constraints which have to be considered in calculating administrative drug dosage.…”

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

“…This task is challenging in closed-loop control of anesthesia, since the gains should be tuned according to the physiological parameters of each patient [263], [264]. While trial and error is common in tuning PID controllers [261], optimization methods, such as genetic algorithms [257], [265], [266] aimed at minimizing the integrated error, can increase the performance significantly [258], [267]. Nevertheless, the physiological parameters of patients vary based on age, weight, disease, and type of surgery being performed, and presently the available patient data is limited and does not adequately depict the physiological parameters of all patients.…”

“…Other investigations have shown benefits from applying a tracking time-constant as the geometric mean of the integral and derivative time-constants of the PID controller [257] and introducing reference shaping (i.e. the desired BIS index is changed from a step input to a specialized profile) [170] and integrator anti-windup [258] to prevent integrator windup during the induction phase. Some alternative approaches that have also been shown to improve the PID controller performance include using an event-based control scheme, which decreases the variations of controller signal and optimally cancels the noise and disturbances, and using an inversionbased methodology, which increases patient safety by reducing BIS overshoot and producing a smoother drug infusion rate [259].…”

PID Controller With Fuzzy Inference System As Simple Feedback For Closed Loop Control Of Intravenous Anesthesia

“…A system identification was performed and validated by a study on 10 patients. The conclusion of their work is that as the model appears to be time variable, a time-variable auto tuning controller consequences to be the optimal controller approach.Khaled Ejaz and Jiann-shiou Yang, together work on "Controlling depth of anesthesia using PID tuning: A comparative Model-Based Study" in 2004 [21]. Three different pharmacokinetic models are used with a discrete PID controller and compared to test effectiveness of PID controller in different patient conditions.AtiehBamdadian and et.al published their research titled "Generalized Predictive Control of depth of anesthesia by using a pharmacokinetic-pharmacodynamic model of the patient" [24].…”