Predictable capability control scheme for oxygen‐exchange blood flow regulation in an extracorporeal membrane oxygenation system

Abstract

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.

Model predictive control optimisation using the metaheuristic optimisation for blood pressure control

“…Haemodynamically, blood pressure is the force that imparts blood to the walls of the veins in which it flows. Normally, blood pressure increases and decreases continuously throughout the day, but if left elevated for a long time, it can damage the heart increasing the risk of heart attack [ 1 , 2 ].…”