Automated Multi-Drugs Administration During Total Intravenous Anesthesia Using Multi-Model Predictive Control

TL;DR

This paper presents a multi-model predictive control system for automating the co-administration of drugs during anesthesia, demonstrating improved robustness over traditional methods through simulations.

Contribution

It introduces a multi-model predictive control approach with multiple Kalman filters for drug administration during anesthesia, handling parameter uncertainties effectively.

Findings

Enhanced robustness in drug control via multi-model approach

Comparable or improved performance over conventional MPC and PID

Open-source implementation available for further research

Abstract

In this paper, a multi-model predictive control approach is used to automate the co-administration of propofol and remifentanil from bispectral index measurement during general anesthesia. To handle the parameter uncertainties in the non-linear output function, multiple Extended Kalman Filters are used to estimate the state of the system in parallel. The best model is chosen using a model-matching criterion and used in a non-linear MPC to compute the next drug rates. The method is compared with a conventional non-linear MPC approach and a PID from the literature. The robustness of the controller is evaluated using Monte-Carlo simulations on a wide population introducing uncertainties in the models. Both simulation setup and controller codes are accessible in open source for further use. Our preliminary results show the potential interest in using a multi-model method to handle parameter uncertainties.