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Closed loop anesthesia delivery (CLAD) systems can help anesthesiologistsefficiently achieve and maintain desired anesthetic depth over an extendedperiod of time. A typical CLAD system would use an anesthetic marker,calculated from physiological signals, as real-time feedback to adjustanesthetic dosage towards achieving a desired set-point of the marker. Sincecontrol strategies for CLAD vary across the systems reported in recentliterature, a comparative analysis of common control strategies can be useful.For a nonlinear plant model based on well-established models of compartmentalpharmacokinetics and sigmoid-Emax pharmacodynamics, we numerically analyze theset-point tracking performance of three output-feedback linear controlstrategies: proportional-integral-derivative (PID) control, linear quadraticGaussian (LQG) control, and an LQG with integral action (ILQG). Specifically,we numerically simulate multiple CLAD sessions for the scenario where the plantmodel parameters are unavailable for a patient and the controller is designedbased on a nominal model and controller gains are held constant throughout asession. Based on the numerical analyses performed here, conditioned on ourchoice of model and controllers, we infer that in terms of accuracy and biasPID 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 smootherinfusion rate while achieving comparable steady-state response with respect toPID. The numerical analyses framework and findings, reported here, can helpCLAD developers in their choice of control strategies. This paper may alsoserve as a tutorial paper for teaching control theory for CLAD.

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