@inproceedings{01JQTJG6ENA4CHSKKJPPR5NTAB,
  abstract     = {{Computer aided control in biomedical applications is gaining more and more popularity due to numerous research studies that have proven the efficiency of automatic control over manual dosing, which is highly susceptible to human errors. Optimal drug dosing is best achieved using automatic control, which triggers important benefits in terms of both costs and patient side-effects. However, mathematical models for patients are highly susceptible to large modeling uncertainty. A predictive control algorithm is designed in this paper for optimal multidrug control of hemodynamic variables. Improved closed loop performance is obtained compared to similar control strategies, for +/- 30% modeling uncertainty. The simulation results demonstrate that predictive control is a feasible solution for optimal drug dosing. An analysis of the closed loop performance for significant patient variability shows that controllers tuned using a nominal patient model often fail to achieve desired robustness. To limit the effect of modeling uncertainty, the prediction model should be updated using an online identification tool to extract patient features.}},
  author       = {{Popescu, Teodora and Birs, Isabela Roxana and Ben Othman, Ghada and Yumuk, Erhan and Mihai, Marcian and Hegedus, Erwin and Copot, Dana and De Keyser, Robain and Ionescu, Clara and Muresan, Cristina I.}},
  booktitle    = {{2024 AMERICAN CONTROL CONFERENCE, ACC 2024}},
  isbn         = {{9798350382662}},
  issn         = {{0743-1619}},
  keywords     = {{multivariable predictive control,uncertainty,optimal multidrug dosing,closed loop control of anesthesia}},
  language     = {{eng}},
  location     = {{Toronto, CANADA}},
  pages        = {{3891--3896}},
  title        = {{Uncertainty and its effect on optimal multidrug control of hemodynamic variables}},
  year         = {{2024}},
}

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