Design and Simulation of a Neuro-Fuzzy Based Temperature Controller for Neonatal Incubator
| dc.contributor.advisor | Shiferaw, Dereje (PhD) | |
| dc.contributor.author | Demerwe, Dagmawi | |
| dc.date.accessioned | 2018-06-28T11:10:52Z | |
| dc.date.accessioned | 2023-11-28T14:20:30Z | |
| dc.date.available | 2018-06-28T11:10:52Z | |
| dc.date.available | 2023-11-28T14:20:30Z | |
| dc.date.issued | 2016-12 | |
| dc.description.abstract | Premature infant’s birth is a worldwide problem. Their organs are not mature enough to allow normal postnatal survival relative to normal babies, consequently they will became hypothermic, which leads them to death. Premature neonates survive in a very narrow core temperature range (36.5-37.5ºC) and suitable relative humidity. As a result, some parameters have to be monitored and their accuracy remains an important matter. Infant incubators are complex medical devices, which are often used immediately after delivery and for the coming few months of their life depending on the infant’s health condition. They use the convection of warm and humidified air to control the temperature of the infant. They have two modes of operation, either the incubator’s air temperature is sensed and used to control the heat flow or infant’s skin temperature is sensed and used in the feedback control system. Infant’s skin temperature control only often leads to large fluctuations in the incubator’s air temperature, similarly incubator’s air temperature control only also leads to infant’s skin temperature fluctuations. This thesis presents the application of adaptive neuro fuzzy inference controller for ATOM V-850 model infant incubator system, in order to control the incubator’s air temperature and the infant’s skin temperature simultaneously. The corresponding fuzzy logic controller is designed for the same system, in order to work with structured knowledge in the form of rules in the FIS. However, there exists no formal framework for the choice of various design parameters and optimization of these parameters generally is done by trial and error technique. The combination of artificial neural networks and fuzzy logic systems offers the possibility of solving tuning problems and design difficulties of fuzzy logic system. The performance comparison between the proposed ANFIS controller and FLC is analyzed through various conditions using MATLAB/Simulink® software. Simulation results show that the performance of the proposed ANFIS Controller, in tracking the desired incubator’s air temperature and desired infant’s skin temperature, improved to 0.39% and 0.2% error from 16.6% and 1.47% error in the FLC respectively. Results also show that, the ANFIS model on the closed loop infant incubator system provides best control performance over a wide range of operating conditions relative to FLC. Key Words: Neonatal incubator, Preterm infant, ANFIS controller, ANN, FLC, MATLAB/Simulink® | en_US |
| dc.description.sponsorship | Addis Ababa University | en_US |
| dc.identifier.uri | http://etd.aau.edu.et/handle/12345678/4618 | |
| dc.language.iso | en | en_US |
| dc.subject | Neonatal incubator; Preterm infant; ANFIS controller; ANN, FLC; MATLAB/Simulink | en_US |
| dc.title | Design and Simulation of a Neuro-Fuzzy Based Temperature Controller for Neonatal Incubator | en_US |
| dc.type | Thesis | en_US |