Ventilator Control Based on a Fuzzy-Neural Network Approach

Respiratory systems are complex nonlinear systems that exhibit uncertain properties. Since patients´ needs are not constant acceptable control of ventilator has to be adaptive to ensure that patients with severe pulmonary disease will obtain the required ventilation support. The AUTOPILOT-BT system was developed to reduce cognitive load of intensivists and at the same time improve mechanical ventilation therapy [1]. The goal of this research was to evaluate a nonlinear adaptive fuzzy-neural network controller, in which a fuzzy controller is used to control the flow and a neural network is used to identify the nonlinear respiratory system. The proposed nonlinear intelligent controller is applied to data recorded during a multicenter study on ARDS patients (adult respiratory distress syndrome). The experimental results demonstrate that this adaptive fuzzy-neural network controller has much better control performance than is obtained with traditional controllers.