Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators

Abstract

Membrane oxygenators are devices that benefit from automatic control. This is especially true for implantable membrane oxygenators—a class of wearable rehabilitation devices that show high potential for fast recovery after lung injury. We present a performance comparison for reference tracking of carbon dioxide partial pressure between three control algorithms—a classical proportional-integral (PI) controller, a modern non-linear model predictive controller, and a novel deep reinforcement learning controller. The results are based on simulation studies of an improved compartmental model of a membrane oxygenator. The compartmental model of the oxygenator was improved by decoupling the oxygen kinetics from the system and only using the oxygen saturation as an input to the model. Both the gas flow rate and blood flow rate were used as the manipulated variable of the controllers. All three controllers were able to track references satisfactorily, based on several performance metrics. The PI controller had the fastest response, with an average rise time and settling time of 1.18 s and 2.24 s and the lowest root mean squared error of 1.06 mmHg. The NMPC controller showed the lowest steady state error of 0.17 mmHg and reached the reference signal with less than 2% error in 90% of the cases within 15 s. The PI and RL reached the reference with less than 2% error in 84% and 50% of the cases, respectively, and showed a steady state error of 0.29 mmHg and 0.5 mmHg.