A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery
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1-s2.0-S2405896320306534-main.pdf
Description
Published version
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1.13 MB
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Adobe PDF
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4e635d1441f4b86599faf0d2e5c530ba
Author(s)
Chakravarty, Sourish
Waite, Ayan S
Abel, John H
Brown, Emery Neal
Date Issued
2020
Journal
IFAC-PapersOnLine
Publisher
Elsevier BV
Citation
Chakravarty, Sourish, Waite, Ayan S, Abel, John H and Brown, Emery N. 2020. "A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery." IFAC-PapersOnLine, 53 (2).
Version
Final published version
Abstract
Closed loop anesthesia delivery (CLAD) systems can help anesthesiologists efficiently achieve and maintain desired anesthetic depth over an extended period of time. A typical CLAD system would use an anesthetic marker, calculated from physiological signals, as real-time feedback to adjust anesthetic dosage towards achieving a desired set-point of the marker. Since control strategies for CLAD vary across the systems reported in recent literature, a comparative analysis of common control strategies can be useful. For a nonlinear plant model based on well-established models of compartmental pharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze the set-point tracking performance of three output-feedback linear control strategies: proportional-integral-derivative (PID) control, linear quadratic Gaussian (LQG) control, and an LQG with integral action (ILQG). Specifically, we numerically simulate multiple CLAD sessions for the scenario where the plant model parameters are unavailable for a patient and the controller is designed based on a nominal model and controller gains are held constant throughout a session. Based on the numerical analyses performed here, conditioned on our choice of model and controllers, we infer that in terms of accuracy and bias PID control performs better than ILQG which in turn performs better than LQG. In the case of noisy observations, ILQG can be tuned to provide a smoother infusion rate while achieving comparable steady state response with respect to PID. The numerical analysis framework and findings reported here can help CLAD developers in their choice of control strategies. This paper may also serve as a tutorial paper for teaching control theory for CLAD.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1016/J.IFACOL.2020.12.369
