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Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia

Dana Copot (UGent) , Frederik Kussé, Maria Ghita (UGent) , Mihaela Ghita (UGent) , Martine Neckebroek (UGent) and Anca Maxim (UGent)
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Abstract
In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for anaesthesia procedure is proposed. The algorithm is tested in simulation on a hypnosis-hemodynamic combined model for use during general anesthesia. The preliminary results are promising and show the effectiveness of the control procedure.
Keywords
nociceptor stimulus, hypnosis, mean arterial pressure, distributed predictive control, MPC, multivariable control

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MLA
Copot, Dana, et al. “Distributed Model Predictive Control for Hypnosis-Hemodynamic Maintenance during Anesthesia.” 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), IEEE, 2019, pp. 638–43, doi:10.1109/icstcc.2019.8885554.
APA
Copot, D., Kussé, F., Ghita, M., Ghita, M., Neckebroek, M., & Maxim, A. (2019). Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia. In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC) (pp. 638–643). Sinaia, Romania: IEEE. https://doi.org/10.1109/icstcc.2019.8885554
Chicago author-date
Copot, Dana, Frederik Kussé, Maria Ghita, Mihaela Ghita, Martine Neckebroek, and Anca Maxim. 2019. “Distributed Model Predictive Control for Hypnosis-Hemodynamic Maintenance during Anesthesia.” In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), 638–43. IEEE. https://doi.org/10.1109/icstcc.2019.8885554.
Chicago author-date (all authors)
Copot, Dana, Frederik Kussé, Maria Ghita, Mihaela Ghita, Martine Neckebroek, and Anca Maxim. 2019. “Distributed Model Predictive Control for Hypnosis-Hemodynamic Maintenance during Anesthesia.” In 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), 638–643. IEEE. doi:10.1109/icstcc.2019.8885554.
Vancouver
1.
Copot D, Kussé F, Ghita M, Ghita M, Neckebroek M, Maxim A. Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia. In: 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC). IEEE; 2019. p. 638–43.
IEEE
[1]
D. Copot, F. Kussé, M. Ghita, M. Ghita, M. Neckebroek, and A. Maxim, “Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia,” in 2019 23rd International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, Romania, 2019, pp. 638–643.
@inproceedings{8667166,
  abstract     = {In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for anaesthesia procedure is proposed. The algorithm is tested in simulation on a hypnosis-hemodynamic combined model for use during general anesthesia. The preliminary results are promising and show the effectiveness of the control procedure.},
  author       = {Copot, Dana and Kussé, Frederik and Ghita, Maria and Ghita, Mihaela and Neckebroek, Martine and Maxim, Anca},
  booktitle    = {2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)},
  isbn         = {9781728106991},
  keywords     = {nociceptor stimulus,hypnosis,mean arterial pressure,distributed predictive control,MPC,multivariable control},
  language     = {eng},
  location     = {Sinaia, Romania},
  pages        = {638--643},
  publisher    = {IEEE},
  title        = {Distributed model predictive control for hypnosis-hemodynamic maintenance during anesthesia},
  url          = {http://dx.doi.org/10.1109/icstcc.2019.8885554},
  year         = {2019},
}

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