Model predictive control of anesthesia under uncertainty Academic Article

abstract

• 2014 Elsevier Ltd. This paper addresses inter- and intra-patient variability in the context of automated drug delivery during anesthesia. A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability. The parameter with the highest sensitivity, C50 the effect site concentration at 50% drug effect, is estimated online. The performance of the closed loop control design is shown for induction and maintenance of volatile anesthesia. In the maintenance phase, the control strategy is evaluated for predefined disturbances that are commonly occurring during surgery. The presented approach shows an improved performance compared to the nominal MPC controller under uncertainty.

authors

• Pistikopoulos, Stratos

published proceedings

• COMPUTERS & CHEMICAL ENGINEERING

author list (cited authors)

• Krieger, A., & Pistikopoulos, E. N.

citation count

• 40

complete list of authors

• Krieger, Alexandra||Pistikopoulos, Efstratios N

publication date

• December 2014

publisher

• Elsevier  Publisher

published in

• Computers and Chemical Engineering  Journal

keywords

• Anesthesia
• Model Predictive Control
• Multi-parametric Model Predictive Control
• Online Parameter Estimation
• Patient Variability
• Uncertainty

Digital Object Identifier (DOI)

• 10.1016/j.compchemeng.2014.07.025

start page

• 699

end page

• 707

volume

• 71

URL

• http://dx.doi.org/10.1016/j.compchemeng.2014.07.025