A simulation-based comparative analysis of PID and LQG control for closed-loop anesthesia delivery

TL;DR

This paper compares PID, LQG, and ILQG control strategies for closed-loop anesthesia delivery using numerical simulations, providing insights into their accuracy, bias, and noise handling capabilities to guide system development.

Contribution

It offers a systematic numerical comparison of common control strategies for CLAD, highlighting their relative performance and tuning considerations.

Findings

PID outperforms ILQG and LQG in accuracy and bias.

ILQG provides smoother control under noisy conditions.

The framework aids in selecting control strategies for CLAD development.

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 analyses 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.