Open AccessPredictable capability control scheme for oxygen‐exchange blood flow regulation in an extracorporeal membrane oxygenation system
Author(s) -
Kan ChungDann,
Chen WeiLing,
Lin ChiaHung,
Chen YingShin
Publication year - 2017
Publication title -
iet systems biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.367
H-Index - 50
eISSN - 1751-8857
pISSN - 1751-8849
DOI - 10.1049/iet-syb.2017.0008
Subject(s) - extracorporeal membrane oxygenation , control theory (sociology) , extracorporeal circulation , membrane oxygenator , controller (irrigation) , computer science , servo , flow control (data) , cannula , engineering , anesthesia , medicine , surgery , control (management) , artificial intelligence , agronomy , biology , computer network
Extracorporeal membrane oxygenation system is used for rescue treatment strategies for temporary cardiopulmonary function support to facilitate adequately oxygenated blood to return into the systemic and pulmonary circulation systems. Therefore, a servo flow regulator is used to adjust the roller motor speed, while support blood flow can match the sweep gas flow (GF) in a membrane oxygenator. A generalised regression neural network is designed as an estimator to automatically estimate the desired roller pump speed and control parameters. Then, the proportional–integral–derivative controller with tuning control parameters showed good performance to achieve speed regulation and speed tracking in the desired operating point. Given the pressure of carbon dioxide, drainage blood flow, and cannula size, the proposed predictable capability control scheme can be validated to meet the intended uses in clinical applications.