This paper presents a Model Predictive Control (MPC) approach for Depth-of-Hypnosis (DoH) control in Total IntraVeneous Anesthesia (TIVA), where the Bispectral Index (BIS) signal is the process variable. In particular, a Branch and Bound (BnB) algorithm is employed after the induction phase for the identification of the parameters of the propofol pharmacokinetic/pharmacodynamic (PK/PD) model for the patient. Then, a suitably designed predictive controller based on that model is applied during the maintenance phase so that the robustness to inter-patient variability is properly addressed. Extensive simulation results show the effectiveness of the individualized approach.
Patient-specific MPC for improved robustness in anesthesia / Milanesi, M., Consolini, L., Credico, G.D., Latronico, N., Laurini, M., Paltenghi, M., Schiavo, M., Visioli, A.. - In: COMMUNICATIONS IN NONLINEAR SCIENCE & NUMERICAL SIMULATION. - ISSN 1007-5704. - 162:110210(2026). [10.1016/j.cnsns.2026.110210]
Patient-specific MPC for improved robustness in anesthesia
Milanesi M.;Consolini L.;Latronico N.;Laurini M.;Visioli A.
2026-01-01
Abstract
This paper presents a Model Predictive Control (MPC) approach for Depth-of-Hypnosis (DoH) control in Total IntraVeneous Anesthesia (TIVA), where the Bispectral Index (BIS) signal is the process variable. In particular, a Branch and Bound (BnB) algorithm is employed after the induction phase for the identification of the parameters of the propofol pharmacokinetic/pharmacodynamic (PK/PD) model for the patient. Then, a suitably designed predictive controller based on that model is applied during the maintenance phase so that the robustness to inter-patient variability is properly addressed. Extensive simulation results show the effectiveness of the individualized approach.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
