Conference Paper

Pressure- and Work-Limited Neuroadaptive Control for Mechanical Ventilation of Critical Care Patients

Sch. of Aerosp. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
DOI: 10.1109/CDC.2010.5717726 Conference: Decision and Control (CDC), 2010 49th IEEE Conference on
Source: DBLP

ABSTRACT

In this paper, we develop a neuroadaptive control architecture to control lung volume and minute ventilation with input pressure constraints that also accounts for spontaneous breathing by the patient. Specifically, we develop a pressure-and work-limited neuroadaptive controller for mechanical ventilation based on a nonlinear multi-compartmental lung model. The control framework does not rely on any averaged data and is designed to automatically adjust the input pressure to the patient's physiological characteristics capturing lung resistance and compliance modeling uncertainty. Moreover, the controller accounts for input pressure constraints as well as work of breathing constraints. Finally, the effect of spontaneous breathing is incorporated within the lung model and the control framework.

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    • "This enables more sophisticated control architectures to effectively employ output feedback techniques to provide adequate minute ventilation with limited input pressure [10]– [12]. Specifically, a reference volume pattern can be specified by the clinician and the controller can apply a limited input pressure so that the total lung volume tracks the reference volume pattern during inspiration and expiration phases. "
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    ABSTRACT: In this article, we develop an output feedback adaptive sliding mode controller for a general multicompartment lung mechanics model with nonlinear resistance and compliance respiratory parameters. Specifically, for a given clinically plausible reference volume pattern, we develop an adaptive sliding mode controller that accounts for input pressure and rate saturation constraints to automatically adjust the applied input so that the total lung volume tracks the given reference pattern. The proposed tracking control framework is applied to a two-compartment lung mechanics model with nonlinear lung compliance and resistance parameters.
    Full-text · Conference Paper · Dec 2014
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    • "Rohrer's equation is used to describe airway resistances as a function of air flow [10]–[13] and, in general, provides a more accurate account of the resistive losses as a function of flows as compared to a linear resistive model over a large range of flows. Several illustrative numerical examples for a two-compartment lung model are presented and the response of the fully nonlinear multicompartment lung model to an arbitrary applied pressure is compared to that of a multicompartment lung model with linear resistances and nonlinear compliances [9]. The notation used in this paper is standard. "
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    ABSTRACT: In this paper, we develop a nonlinear multicompartment lung mechanics model that accounts for nonlinearities in both the airway resistances and the lung compliances. Many models assume that the airway resistances for a lung mechanics system are constant over the entire range of air flows, and hence, pressure losses due to the airway resistances are assumed to be linear functions of the air flows. In the development of our nonlinear multicompartment lung model, we assume that the resistive losses are characterized by a Rohrer-type model, which can more accurately capture resistive losses as a function of the flows. Several illustrative numerical examples for a two-compartment lung model are presented and the response of the multicompartment lung model with nonlinear resistances and nonlinear compliances is compared to that of a multicompartment lung model with linear resistances and nonlinear compliances.
    Full-text · Conference Paper · Jun 2014
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    • "With the increasing availability of microchip technology, it has been possible to design partially automated mechanical ventilators with control algorithms for providing volume or pressure control [1] [2] [3] [4] [5]. More sophisticated fully automated model reference adaptive control algorithms for mechanical ventilation have also been recently developed [6] [7]. These algorithms require a reference model for identifying a clinically plausible breathing pattern. "
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    ABSTRACT: We develop optimal respiratory airflow patterns using a nonlinear multicompartment model for a lung mechanics system. Specifically, we use classical calculus of variations minimization techniques to derive an optimal airflow pattern for inspiratory and expiratory breathing cycles. The physiological interpretation of the optimality criteria used involves the minimization of work of breathing and lung volume acceleration for the inspiratory phase, and the minimization of the elastic potential energy and rapid airflow rate changes for the expiratory phase. Finally, we numerically integrate the resulting nonlinear two-point boundary value problems to determine the optimal airflow patterns over the inspiratory and expiratory breathing cycles.
    Full-text · Article · Jun 2012 · Computational and Mathematical Methods in Medicine
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