Funnel control for oxygenation during artificial ventilation therapy

Pomprapa, Anake; Alfocea, Sergio Ramón; Göbel, Christof; Misgeld, Berno J.E.; Leonhardt, Steffen

doi:10.3182/20140824-6-za-1003.00886

Cited by 17 publications

(12 citation statements)

References 15 publications

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“…To optimize PEEP, many titration techniques have been developed; for instance, using the stress index [ 27 , 28 ], the assessment of transpulmonary pressure [ 29 ], or the optimization of ventilation homogeneity by EIT [ 30 ]. Owing to possible oxygen toxicity by excess FiO 2 [ 31 ], a feedback control system for regulating oxygenation to prespecified targets using FiO 2 [ 32 ] may thus use any combination of the protocols described above for PEEP titration. Hyperoxia shall then be avoided.…”

Section: Discussionmentioning

confidence: 99%

Automatic protective ventilation using the ARDSNet protocol with the additional monitoring of electrical impedance tomography

et al. 2014

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IntroductionAutomatic ventilation for patients with respiratory failure aims at reducing mortality and can minimize the workload of clinical staff, offer standardized continuous care, and ultimately save the overall cost of therapy. We therefore developed a prototype for closed-loop ventilation using acute respiratory distress syndrome network (ARDSNet) protocol, called autoARDSNet.MethodsA protocol-driven ventilation using goal-oriented structural programming was implemented and used for 4 hours in seven pigs with lavage-induced acute respiratory distress syndrome (ARDS). Oxygenation, plateau pressure and pH goals were controlled during the automatic ventilation therapy using autoARDSNet. Monitoring included standard respiratory, arterial blood gas analysis and electrical impedance tomography (EIT) images. After 2-hour automatic ventilation, a disconnection of the animal from the ventilator was carried out for 10 seconds, simulating a frequent clinical scenario for routine clinical care or intra-hospital transport.ResultsThis pilot study of seven pigs showed stable and robust response for oxygenation, plateau pressure and pH value using the automated system. A 10-second disconnection at the patient-ventilator interface caused impaired oxygenation and severe acidosis. However, the automated protocol-driven ventilation was able to solve these problems. Additionally, regional ventilation was monitored by EIT for the evaluation of ventilation in real-time at bedside with one prominent case of pneumothorax.ConclusionsWe implemented an automatic ventilation therapy using ARDSNet protocol with seven pigs. All positive outcomes were obtained by the closed-loop ventilation therapy, which can offer a continuous standard protocol-driven algorithm to ARDS subjects.

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Section: Discussionmentioning

confidence: 99%

Automatic protective ventilation using the ARDSNet protocol with the additional monitoring of electrical impedance tomography

et al. 2014

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“…There were a number of potential controllers applied for the control of oxygenation, for example a proportionalintegral (PI) controller with gain scheduling (Walter et al (2009)), a Smith predictor with internal PI controller (Lüpschen et al (2007)), a knowledge-based controller (Pomprapa et al (2013a)), potentially a self-tuning adaptive controller (Pomprapa et al (2010)) or a funnel controller (Pomprapa et al (2014a)). The settling time was approximately 300 s by a PI controller and 250 s by a Smith predictor for the control of oxygenation in extracoporeal membrane oxygenator (ECMO) (Walter et al (2009)).…”

Section: Discussionmentioning

confidence: 99%

“…In such critical situations of oxygen deficiency, oxygen therapy is required by adjusting the fraction of inspired oxygen (FiO 2 ) in order to maintain tissue and brain function (Claure and Bancalari (2013)). Those patients require not only the stabilization but also an improvement of the oxygenation, which can be measured in terms of arterial oxygen tension (PaO 2 ) or arterial oxygen saturation (SaO 2 ) (Pomprapa et al (2014a)). Therefore, we focus on hypoxia management, which results in a single-input single-output (SISO) system during mechanical ventilation therapy.…”

Section: Introductionmentioning

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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“…The new funnel control strategy has a potential impact on various applications. Since its development in [30], the funnel controller proved an appropriate tool for tracking problems in various applications such as temperature control of chemical reactor models [33], control of industrial servo-systems [24] and underactuated multibody systems [9], speed control of wind turbine systems [21,23], current control for synchronous machines [22], DC-link power flow control [52], voltage and current control of electrical circuits [14], oxygenation control during artificial ventilation therapy [45], control of peak inspiratory pressure [46] and adaptive cruise control [12,13].…”

Section: Applicationsmentioning

confidence: 99%

Funnel control of nonlinear systems

Berger

Ilchmann

Ryan

2021

Math. Control Signals Syst.

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Tracking of reference signals is addressed in the context of a class of nonlinear controlled systems modelled by r-th-order functional differential equations, encompassing inter alia systems with unknown “control direction” and dead-zone input effects. A control structure is developed which ensures that, for every member of the underlying system class and every admissible reference signal, the tracking error evolves in a prescribed funnel chosen to reflect transient and asymptotic accuracy objectives. Two fundamental properties underpin the system class: bounded-input bounded-output stable internal dynamics, and a high-gain property (an antecedent of which is the concept of sign-definite high-frequency gain in the context of linear systems).

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Funnel control for oxygenation during artificial ventilation therapy

Cited by 17 publications

References 15 publications

Automatic protective ventilation using the ARDSNet protocol with the additional monitoring of electrical impedance tomography

Automatic protective ventilation using the ARDSNet protocol with the additional monitoring of electrical impedance tomography

Policy Iteration Algorithm for the Control of Oxygenation

Funnel control of nonlinear systems

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