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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    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.
    Computational and Mathematical Methods in Medicine 01/2012; 2012:165946. · 0.79 Impact Factor

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