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.

Download full-text

Full-text

Available from: James M Bailey, Aug 21, 2015
0 Followers
 · 
102 Views
  • Source
    • "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. "
    [Show abstract] [Hide abstract]
    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 06/2012; 2012:165946. DOI:10.1155/2012/165946 · 1.02 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, an output-feedback sliding mode control (SMC) for a linear multicompartment respiratory system is developed. Since the applied input pressure to the lungs is in general nonnegative and cannot be arbitrarily large, as not to damage the lungs, a sliding mode control with bounded nonnegative control inputs is proposed. The controller only uses output information (i.e., the total volume of the lungs) and automatically adjusts the applied input pressure so that the system is able to track a given reference signal in the presence of parameter uncertainty (i.e., modelling uncertainty of the lung resistances and lung compliances) and system disturbances. Controllers for both matched and unmatched uncertainty are presented. Specifically, a Lyapunov-based approach is presented for the stability analysis of the system and the proposed control framework is applied to a two-compartment lung model to show the efficacy of the proposed control method.
    Control Applications (CCA), 2013 IEEE International Conference on; 08/2013
  • [Show abstract] [Hide abstract]
    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.
    American Control Conference, 2014; 06/2014
Show more