Backstepping Control with Radial Basis Function Network for a Nonlinear Cardiopulmonary System

Oxygen therapy plays a vital role to recover a patient from severe hypoxia as well as to minimize the risk of hypoxia in a critical situation. Based on this therapeutic technique, this article presents an application of backstepping control for the oxygenation in a cardiopulmonary system. A nonlinear multi-compartment system with unknown hysteresis is used as a human model in this study. With no a priori knowledge of the underlying system dynamics, a radial basis function (RBF) network is integrated into a closed-loop subsystem and trained to identify the unknown nonlinear functions. Consequently, a backstepping controller is designed based on the Lyapunov stability theorem for regulating oxygenation. The theoretical framework and simulation are presented and demonstrated in terms of stability and control performance under the presence of simulated physiological changes, possibly caused by pathophysiological effects in the cardiopulmonary system i.e. critically ill patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).