Self-calibrating total-mass controller for the neuromuscular blockade matching the anesthesiologists' mindset

Abstract

A self-calibrating total-mass controller for the neuromuscular blockade (NMB) based on the minimally parameterized parsimonious (MPP) Wiener model is presented. Using input-output data collected from the initial bolus administration until recovery, the parameters of the MPP model that are used for the controller design are chosen from a set of fifty realistic models for the effect of the muscle relaxant rocuronium in the NMB. In order to overcome modeling uncertainties a numeric self-calibration of the parameters in the MPP model nonlinearity was implemented. This step matches the anesthesiologists' mindset. The controller was extensively tested in simulation and in real cases. The good reference tracking results show the reliability of the proposed strategy and supports its use in the clinical practice.

Performance of an Adaptive Controller for the Neuromuscular Blockade Based on Inversion of a Wiener Model

“…This approach enhances the strategy in since the identification of the nonlinear parameter is not pointwise, but recursive, which results in a more robust identification. The controller was implemented in the platform Galeno and tested in simulation and in real cases.…”

“…The transformation of the control law into discrete‐time was proposed in , and its complete mathematical realization may be found in . The discrete‐time positive compartmental control law is hence given by …”

“…A train‐of‐four (TOF) electrical stimulation of a peripheral muscle ( e.g . the adductor pollicis in the hand of the patient) is a method commonly adopted in studies with closed‐loop NMB control due to its ease of use.…”