Linear MPC for anesthesia process with external predictor

Pawłowski, A.; Schiavo, Michele; Latronico, Nicola; Paltenghi, Massimiliano; Visioli, Antonio

doi:10.1016/j.compchemeng.2022.107747

Cited by 13 publications

(23 citation statements)

References 37 publications

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“…The introduced control system is a generalization and extension to the MISO case of the architecture presented in [40]. The main idea consists in the computation of the inverse of the nonlinear function present in the Wiener model and in the use of a linear GPC algorithm.…”

Section: Control System Architecture Using Miso Approach For Drug Coa...mentioning

confidence: 99%

“…When the filter of the induction phase is operating, the other one, for the maintenance phase, is in tracking mode assuring that its output is changed to be the same as i(t) at the switching time instant. By following the same approach used in [10,40,60], the tuning is performed using an optimization technique, in order to minimize the worst-case IAE (Integrated Absolute Error) taking into account the representative population of the 13 patients described in [10]. The optimization is performed by means of a genetic algorithm, where the initial population (of 40 elements) was generated using a uniform distribution provided by the a Gaussian mutation function.…”

Section: Control Scheme Parameters Tuningmentioning

confidence: 99%

“…In this paper, we propose an MPC system for the MISO process where the nonlinear propofol-remifentanil interaction model on the BIS is exploited [18]. The method suitably generalizes the idea proposed in [40], where the propofol is the only used drug. The control system employs an external predictor to compute the inverse of the static nonlinear element (represented as the Hill surface) of the drug interaction model, which enables the application of the linear MPC technique.…”

Section: Introductionmentioning

confidence: 99%

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Model predictive control using MISO approach for drug co-administration in anesthesia

Pawłowski

Schiavo²,

Latronico³

et al. 2022

Journal of Process Control

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Section: Control System Architecture Using Miso Approach For Drug Coa...mentioning

confidence: 99%

Section: Control Scheme Parameters Tuningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model predictive control using MISO approach for drug co-administration in anesthesia

Pawłowski

Schiavo²,

Latronico³

et al. 2022

Journal of Process Control

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“…The main goal of this study is to provide a quantitative measure that reflects the performance degradation due to the noise presence when compared to noise-free case. The considered MPC control architecture has been previously introduced in [11] and it exploits a realistic nonlinear patient model. The original structure is extended here with a suitably designed additional low-pass filter that attenuates the process noise.…”

Section: 𝑃𝐾(𝑠) 𝑃𝐷(𝑠) 𝐻mentioning

confidence: 99%

“…The GPC-based control scheme proposed in [11] with the additional filter F n , for a noise attenuation.…”

Section: Patient Modelmentioning

confidence: 99%

MPC for Propofol Anesthesia: the Noise Issue

Pawłowski

Schiavo

Latronico

et al. 2022

2022 IEEE Conference on Control Technology and Applications (CCTA)

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The design of automatic control systems for general anesthesia is a challenging task due to the severe safety requirements and process constraints. This is even more complex when model-based control techniques are used due to the significant variability of the process model. Additionally, issues like noisy measurements and interference also influence the control system overall performance. In this context, adequate filtering and control system sampling period selection should be analyzed to test their influence on the controller. In this paper, an MPC system for the depth of hypnosis, where the BIS signal is used as a controlled variable, is analyzed. The main purpose is to test and evaluate how the process noise affects the performance of the control system. The analysis is performed in a simulation study using a dataset of virtual patients representative of a wide population. Results show that a satisfactory performance is obtained when the noise is explicitly taken into account in the controller tuning procedure for a specific sampling period.

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Drug co‐administration in anesthesia using event‐based MPC

Pawłowski,

Schiavo,

Latronico

et al. 2023

Intl J Robust & Nonlinear

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In this article, a predictive event‐based control architecture for multivariable drug infusion in the anesthesia process is presented. The control scheme considers a multiple‐input single‐output process, where the depth of hypnosis is the controlled variable and the infusion rates of propofol and remifentanil, which are co‐administered by imposing a fixed ratio between them, are the control variables. The control system is built on the top of a nonlinear pharmacokinetic/pharmacodynamic model for drug co‐administration that reflects the super‐additive effect of both drugs on the bispectral index scale (BIS), which is a measure of the hypnotic state of the patient. Further, in order to compensate the nonlinear behavior of the system, it exploits an external predictor that is designed to increase the robustness to inter‐/intra‐patient variability. The overall controller parameters are tuned by applying an optimization method on a representative set of virtual patients. The main feature of the proposed control algorithm consists in its asynchronous execution, which can be beneficial in reducing control signal changes due to the noisy measurements of the BIS. The evaluation of the analyzed control architecture through a simulation study reveals that control action variations can be reduced by about 48% on average with respect to its time‐based counterpart. This reduction is obtained at the expense of a lower control accuracy, resulting in a degradation of 18% in terms of integrated absolute error. Results are acceptable from a clinical practice perspective proving that the proposed approach is a feasible alternative to classical time‐based predictive controllers.

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Linear MPC for anesthesia process with external predictor

Cited by 13 publications

References 37 publications

Model predictive control using MISO approach for drug co-administration in anesthesia

Model predictive control using MISO approach for drug co-administration in anesthesia

MPC for Propofol Anesthesia: the Noise Issue

Drug co‐administration in anesthesia using event‐based MPC

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