Cascaded PID controller for anaesthesia delivery

Simalatsar, Alena; Guidi, Monia; Buclin, Thierry

doi:10.1109/embc.2016.7590757

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…Therefore, recent studies are trying to address these problems to achieve comparable performance to other more advanced controllers. For instance, in [255] it is shown that the low-pass filter, included in (40), reduces the sensitivity of the derivative term to random changes. Furthermore, introducing zero reference weight for the proportional and derivative control terms (i.e.…”

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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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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“…Gentilini et al (2001) present two complicated closed-loop controllers for the delivery of inhaled anesthetic isoflurane: one based on the monitoring of arterial pressure and another one of DOH through BIS. For propofol, an IV anesthetic agent often preferred to inhaled gases, an adaptive cascaded PID controller for the target driven delivery has been proposed (Simalatsar et al, 2016). The control variable is the plasma concentration of propofol.…”

Section: Related Workmentioning

confidence: 99%

Robustness analysis of personalised delivery rate computation for IV administered anesthetic

et al. 2018

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Controlled delivery of intravenous (IV) anesthetics aims at fast and safe achievement and maintenance of a suitable depth of hypnosis (DOH), by ensuring appropriate effect site (i.e. brain) exposure to the drug. Today, such drugs are regularly injected by Target Controlled Infusion (TCI) systems, piloted by an open-loop algorithm based on Pharmacokinetic (PK) models. Yet the inaccuracy of concentration prediction of current TCI can reach up to 100%. The situation could be improved by closing the loop with sensors providing regular real measurements of the anesthetic concentration in body fluids. In this paper we present a closed-loop algorithm based on the classic open-loop algorithm combined with a Kalman filter. The latter estimates plasma drug concentration based on PK model and sensor measurements. The estimates are then used in the open-loop algorithm. To validate our approach measurements are generated by means of modulating the population-based plasma concentration values with the maximum inter-and intrapatient variability of the statistical Eleveld‫׳‬s (Eleveld et al., 2014) PK model. This allows us to stress the system to a maximum level prior to testing it on patients. We also perform robustness analysis of this algorithm by accounting for realistic measurement periods and delays.

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“…Ren analyzed the system based on the classical control theory and proved that the cascade PID was better than the traditional single-stage PID control from an amplitude margin and a phase angle margin [29]. A cascade PID is one of the effective methods used to improve the quality of traditional PID control and it has been widely used in the field of process control [30][31][32]. For the cascade PID control of terrain-following UAVs, how to set the control feedback state quantity and how to adjust its parameters are important issues.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

et al. 2021

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For the application of the autonomous guidance of a quadrotor from confined undulant ground, terrain-following is the major issue for flying at a low altitude. This study has modified the open-source autopilot based on the integration of a multi-sensor receiver (a Global Navigation Satellite System (GNSS)), a Lidar-lite (a laser-range-finder device), a barometer and a low-cost inertial navigation system (INS)). These automatically control the position, attitude and height (a constant clearance above the ground) to allow terrain-following and avoid obstacles based on multi-sensors that maintain a constant height above flat ground or with obstacles. The INS/Lidar-lite integration is applied for the attitude and the height stabilization, respectively. The height control is made by the combination of an extended Kalman filter (EKF) estimator and a cascade proportional-integral-derivative (PID) controller that is designed appropriately for the noise characteristics of low accuracy sensors. The proposed terrain-following is tested by both simulations and real-world experiments. The results indicate that the quadrotor can continuously navigate and avoid obstacles at a real-time response of reliable height control with the adjustment time of the cascade PID controller improving over 50% than that of the PID controller.

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Cascaded PID controller for anaesthesia delivery

Cited by 7 publications

References 9 publications

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

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

Robustness analysis of personalised delivery rate computation for IV administered anesthetic

Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control

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