Event-based GPC for depth of hypnosis in anesthesia for efficient use of propofol

Pawłowski, A.; Merigo, Luca; Guzmán, José Luis; Visioli, Antonio; Dormido, Sebastián

doi:10.1109/ebccsp.2017.8022826

Cited by 12 publications

(12 citation statements)

References 25 publications

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“…The controller parameters (N, N u , and λ related to the GPC algorithm and T d for the low-pass filter F d ) need to be selected in order to provide the desired performance and to assure the required robustness. It has been decided to tune the parameters for the set-point following and disturbance rejection tasks (namely, for the induction and maintenance phases) separately in a two steps approach [56][57][58][59]. In the first step of tuning procedure, the controller is setup to provide an optimal set-point tracking response.…”

Section: Control Scheme Parameters Tuningmentioning

confidence: 99%

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 Scheme Parameters Tuningmentioning

confidence: 99%

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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“…Simulation results show that the MHE method has better accuracy and reduced overshoot compared to Kalman filtering [276], [277]. Likewise, studies have also shown that the addition of eventbased input and state output correction features to MPC can increase its robustness against noise and decrease the amount of anesthetic drugs administered [278], [279].…”

Section: Automation In Anesthesiamentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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“…The control from [79] employs genetic algorithms for the offline tunning of event-based PID or model-based PID [80]. The work is continued in [81] through implementation of event-based predictive control extended with virtual actuator deadband. The latest demonstrated similar control accuracy with less control system effort compared to classical time-based predictive controllers.…”

Section: Closed-loop Control Of Anesthesiamentioning

confidence: 99%

Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives

et al. 2020

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Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy.INDEX TERMS Closed-loop control, drug-delivery control, anesthesia, (semi-)automated anesthesia delivery, cyber physical medical systems, decision support systems, adaptive control.

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Event-based GPC for depth of hypnosis in anesthesia for efficient use of propofol

Cited by 12 publications

References 25 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

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives

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