Design And Simulation Of A Neuro-fuzzy Based Temperature Controller For Neonatal Incubator

Premature infant’s birth is a worldwide problem. Their organs are not mature enough to allowrnnormal postnatal survival relative to normal babies, consequently they will became hypothermic,rnwhich leads them to death. Premature neonates survive in a very narrow core temperature rangern(36.5-37.5ºC) and suitable relative humidity. As a result, some parameters have to be monitoredrnand their accuracy remains an important matter. Infant incubators are complex medical devices,rnwhich are often used immediately after delivery and for the coming few months of their life dependingrnon the infant’s health condition. They use the convection of warm and humidified air to controlrnthe temperature of the infant. They have two modes of operation, either the incubator’s airrntemperature is sensed and used to control the heat flow or infant’s skin temperature is sensed andrnused in the feedback control system. Infant’s skin temperature control only often leads to largernfluctuations in the incubator’s air temperature, similarly incubator’s air temperature control onlyrnalso leads to infant’s skin temperature fluctuations.rnThis thesis presents the application of adaptive neuro fuzzy inference controller for ATOM V-850rnmodel infant incubator system, in order to control the incubator’s air temperature and the infant’srnskin temperature simultaneously. The corresponding fuzzy logic controller is designed for the samernsystem, in order to work with structured knowledge in the form of rules in the FIS. However,rnthere exists no formal framework for the choice of various design parameters and optimizationrnof these parameters generally is done by trial and error technique. The combination of artificialrnneural networks and fuzzy logic systems offers the possibility of solving tuning problems andrndesign difficulties of fuzzy logic system.rnThe performance comparison between the proposed ANFIS controller and FLC is analyzedrnthrough various conditions using MATLAB/Simulink® software. Simulation results show that thernperformance of the proposed ANFIS Controller, in tracking the desired incubator’s air temperaturernand desired infant’s skin temperature, improved to 0.39% and 0.2% error from 16.6% and 1.47%rnerror in the FLC respectively. Results also show that, the ANFIS model on the closed loop infantrnincubator system provides best control performance over a wide range of operating conditionsrnrelative to FLC.rnKey Words: Neonatal incubator, Preterm infant, ANFIS controller, ANN, FLC, MATLAB/Simulink®

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