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arXiv · 2309.08229

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

Bob Aubouin-Pairault·Mirko Fiacchini·Thao Dang
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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.

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Bob Aubouin-Pairault, Mirko Fiacchini, Thao Dang. 2023-09-15. Automated Multi-Drugs Administration During Total Intravenous Anesthesia Using Multi-Model Predictive Control. https://arxiv.org/abs/2309.08229

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