Models for control of intravenous anesthesia

Soltesz, Kristian; Heusden, Klaske van; Dumont, Guy A.

doi:10.1016/b978-0-12-815975-0.00010-2

Cited by 6 publications

(1 citation statement)

References 104 publications

(164 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Uncertainty can also arise as a consequence of intra-patient variability (e.g. haemodynamics affecting drug distribution and metabolism) and also as a consequence of limited parameter identifiability from underlying modelling experiments [14,15].…”

Section: Patient Modelsmentioning

confidence: 99%

Robust PID control of propofol anaesthesia: Uncertainty limits performance, not PID structure

González-Cava

Carlson

Troeng

et al. 2021

Computer Methods and Programs in Biomedicine

Self Cite

View full text Add to dashboard Cite

Background and Objective: New proposals to improve the regulation of hypnosis in anaesthesia based on the development of advanced control structures emerge continuously. However, a fair study to analyse the real benefits of these structures compared to simpler clinically validated PID-based solutions has not been presented so far. The main objective of this work is to analyse the performance limitations associated with using a filtered PID controller, as compared to a high-order controller, represented through a Youla parameter.Methods: The comparison consists of a two-steps methodology. First, two robust optimal filtered PID controllers, considering the effect of the inter-patient variability, are synthesised. A set of 47 validated paediatric pharmacological models, identified from clinical data, is used to this end. This model set provides representative inter-patient variability Second, individualised filtered PID and Youla controllers are synthesised for each model in the set. For fairness of comparison, the same performance objective is optimised for all designs, and the same robustness constraints are considered. Controller synthesis is performed utilising convex optimisation and gradient-based methods relying on algebraic differentiation. The worst-case performance over the patient model set is used for the comparison.

show abstract

Section: Patient Modelsmentioning

confidence: 99%