Adaptive Sliding Mode Control for a Blower-Based Breathing Simulator with Unknown and Time-Varying Airway Resistance and Compliance

Looking back at history, the world has experienced many pandemics that affect the people experiencing respiratory system, and most recently, the severe devastation of the COVID-19 pandemic. This reflects the reality of life and the global disparity in ventilator availability between high-income countries and low and middle-income countries. Additionally, various simple ventilators and breathing simulators are non-functional with various artificial lungs due to dynamic lung parameter changes during operation. Therefore, this paper proposes an adaptive controller for a novel blower-based breathing simulator to address the challenge of unknown and varying artificial lung parameters. Specifically, an adaptive sliding mode controller has been developed for precise volume control under uncertain conditions. Alongside that, the metric Integral of Absolute Error (IAE) is used to evaluate control performance. The first simulation scenario with three different input signals, which comprise a ramp, parabolic, and sinusoidal, remained permissible errors below 5%. In the other scenario, the blower-based breathing simulator model's control performance with two different airway parameters sets IAE values over 12 cycles. In the 6 initial cycles, the trend of IAE values shows a gradual decrease over cycles. At cycle 7, a change in airway parameters causes a difference in the IAE value, but it quickly decreases in the following cycles. As indicated by this outcome, the proposed controller proves not only efficient when the airway parameters are unknown constants but also adaptable when these parameters change over time.