ROBUST CONTROL OF END-TIDAL CO&lt;sub&gt;2&lt;/sub&gt; USING THE H&lt;sub&gt;&amp;infin;&lt;/sub&gt; LOOP-SHAPING APPROACH

Pomprapa, Anake; Misgeld, Berno J.E.; Sorgato, Verónica; Stollenwerk, André; Walter, Marian; Leonhardt, Steffen

doi:10.14311/ap.2013.53.0895

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…With this promising technique, various clinical applications can be implemented, for example in homebased mechanical ventilation for patients with apnea, in anesthesia, in intensive care unit (ICU) or during the transport of patients with severe hypoxia. In addition, a policy iteration algorithm with H 2 and H ∞ optimal state feedback controller may be introduced for the application in order to improve robustness and disturbance rejection (Al-Tamimi et al (2007); Pomprapa et al (2013b)). …”

Section: Discussionmentioning

confidence: 99%

Policy Iteration Algorithm for the Control of Oxygenation

et al. 2015

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The policy iteration algorithm (PIA) is a quasi non-identifier approach of nonlinear optimal control based on a reinforcement learning and iterative algorithm in order to solve the Hamilton-Jacobi-Bellman (HJB) equation. The synthesized state-feedback controller corresponding to the converged solution should be applicable for the control of cardiopulmonary system. In this article, the simulation results for the control of oxygenation were carried out using a simplified first-order model with time delay based on porcine dynamics. The distinctive results of oxygenation control can then be achieved based on the proposed control strategy. In addition, the practical example of water level for interacting three-tank system, which has the nonlinear dynamics similar to that of the oxygenation, was implemented in order to prove the concept of this control scheme.

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Section: Discussionmentioning

confidence: 99%

Policy Iteration Algorithm for the Control of Oxygenation

et al. 2015

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“…Adjustments are made by varying the level of therapeutic settings on the device including fraction of inspired oxygen (FiO 2 ) (Morozoff and Saif, 2008; Pomprapa et al, 2017), tidal volume (Martinoni et al, 2004), and positive end expiratory pressure (Tehrani, 2012; Flechelles et al, 2013). A wide range of controller types have been applied including model predictive controllers (Fernando et al, 1995; Pomprapa et al, 2017), PID and fuzzy controller (Morozoff and Saif, 2008), robust controllers (Sano et al, 1988; Pomprapa et al, 2013), and rule-based expert systems (Fernando et al, 1995).…”

Section: Closed-loop Systems For Mechanical Ventilationmentioning

confidence: 99%

“…Pomprapa et al (2013) described the input–output relationship of minute ventilation and EtCo 2 and compare a first order linear model and a non-linear model. The authors used root-mean-square-error (RMSE) of an estimated dataset and a “validated” dataset to compare the model results.…”

Section: Closed-loop Systems For Mechanical Ventilationmentioning

confidence: 99%

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

et al. 2019

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Physiological closed-loop controlled medical devices automatically adjust therapy delivered to a patient to adjust a measured physiological variable. In critical care scenarios, these types of devices could automate, for example, fluid resuscitation, drug delivery, mechanical ventilation, and/or anesthesia and sedation. Evidence from simulations using computational models of physiological systems can play a crucial role in the development of physiological closed-loop controlled devices; but the utility of this evidence will depend on the credibility of the computational model used. Computational models of physiological systems can be complex with numerous nonlinearities, time-varying properties, and unknown parameters, which leads to challenges in model assessment. Given the wide range of potential uses of computational patient models in the design and evaluation of physiological closed-loop controlled systems, and the varying risks associated with the diverse uses, the specific model as well as the necessary evidence to make a model credible for a use case may vary. In this review, we examine the various uses of computational patient models in the design and evaluation of critical care physiological closed-loop controlled systems (e.g., hemodynamic stability, mechanical ventilation, anesthetic delivery) as well as the types of evidence (e.g., verification, validation, and uncertainty quantification activities) presented to support the model for that use. We then examine and discuss how a credibility assessment framework (American Society of Mechanical Engineers Verification and Validation Subcommittee, V&V 40 Verification and Validation in Computational Modeling of Medical Devices) for medical devices can be applied to computational patient models used to test physiological closed-loop controlled systems.

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“…Examples would be the automatic ARDSNet protocol system [ 21 ] or the automation of the open lung concept [ 22 ]. Automatic closed-loop control are often focused on either oxygenation [ 23 , 24 ] or ventilation ( P ETCO ) [ 25 – 27 ]. Highly automated systems which combine oxygen and carbon dioxide controllers have also been presented, see for example [ 28 ], or the commercially available system INTELLiVENT ® -ASV (Hamilton Medical AG, Switzerland) [ 29 ].…”

Section: Introductionmentioning

confidence: 99%

SOLVe: a closed-loop system focused on protective mechanical ventilation

et al. 2023

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Background Mechanical ventilation is an essential component in the treatment of patients with acute respiratory distress syndrome. Prompt adaptation of the settings of a ventilator to the variable needs of patients is essential to ensure personalised and protective ventilation. Still, it is challenging and time-consuming for the therapist at the bedside. In addition, general implementation barriers hinder the timely incorporation of new evidence from clinical studies into routine clinical practice. Results We present a system combing clinical evidence and expert knowledge within a physiological closed-loop control structure for mechanical ventilation. The system includes multiple controllers to support adequate gas exchange while adhering to multiple evidence-based components of lung protective ventilation. We performed a pilot study on three animals with an induced ARDS. The system achieved a time-in-target of over 75 % for all targets and avoided any critical phases of low oxygen saturation, despite provoked disturbances such as disconnections from the ventilator and positional changes of the subject. Conclusions The presented system can provide personalised and lung-protective ventilation and reduce clinician workload in clinical practice.

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ROBUST CONTROL OF END-TIDAL CO<sub>2</sub> USING THE H<sub>∞</sub> LOOP-SHAPING APPROACH

Cited by 6 publications

References 16 publications

Policy Iteration Algorithm for the Control of Oxygenation

Policy Iteration Algorithm for the Control of Oxygenation

Credibility Evidence for Computational Patient Models Used in the Development of Physiological Closed-Loop Controlled Devices for Critical Care Medicine

SOLVe: a closed-loop system focused on protective mechanical ventilation

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