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NONLINEAR MODEL PREDICTIVE CONTROL WITH NEURAL NETWORK OPTIMIZATION FOR MECHANICAL VENTILATION OF CRITICAL CARE PATIENTS.

By Ms. S. V. Analin

Abstract

Artificial breathing plays an important game in human life in the case of respiratory failure. Causes of respiratoryfailure will harvest instability in patient’s breathing condition due to n onlinearity of gas exchange in various alveoli sac of the lung and henceforth it is necessary to pay a careful attention for several comparetmental model of the lung during ventilation. This paper presents a solution for the spontaneous breathing of critical care patients under mechanical ventilation using a novel nonlinear model predictive controller enhanced by a neural network optimization. Knowledge of nonlinear dynamic system provides the way to design the mathematical view of a multi compartment lung model, which act as a plant model for the controller. Specifically the control input pressure relies on patient’s physiological characteristics capturing lung resistance and compliance uncertainty of an individual compartment of the designed lung model and the various response of the respiratory system can be analyzed for determining the effectiveness of this controller. Finally the potency of controller action ties with safety constraint limit such as saturation and integral constrain into a single package and show their performance on the designed lung model for the sake smooth spontaneous breathing of the patients

Topics: system, neural network
Year: 2014
OAI identifier: oai:CiteSeerX.psu:10.1.1.416.8317
Provided by: CiteSeerX
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