Iterative Learning Control Applied to a Recently Proposed Mechanical Ventilator Topology

摘要

Mechanical ventilators are machines used to assist breathing and are widely used in research involving respiratory diseases. However, most of the commercial options available for small animals are limited when tracking ventilatory profiles such as desired air pressure or flow. Iterative learning control (ILC) is a control technique that aims to improve performance of systems with repetitive tasks by learning on previous executions. This study investigates the performance of an ILC algorithm applied on the problem of tracking pressure profiles associated with a commonly used ventilatory mode. We feedback linearize the ventilator system dynamics and design a PI controller and a simple ILC algorithm that satisfies convergence, stability and final error properties. We also consider a hypothetical periodic disturbance representing a possible leakage. We show that ILC alone is not suitable to replace a conventional feedback controller. Nevertheless, ILC combined with feedback control can significantly improve performance.