Robust Control of Exo-Abs, a Wearable Platform for Ubiquitous Respiratory Assistance

Existing noninvasive breathing assist options compatible with out-of-hospital settings are limited and not appropriate to enable essential everyday activities, thereby deteriorating the quality of life. In our prior work, we developed the Exo-Abs, a novel wearable robotic platform for ubiquitous assistance of respiratory functions in patients with respiratory deficiency. This paper concerns the development of a model-based closed-loop control algorithm for the Exo-Abs to automate its breathing assistance. To facilitate model-based development of closed-loop control algorithms, we developed a control-oriented mathematical model of the Exo-Abs. Then, we developed a robust absolutely stabilizing gain-scheduled proportional-integral control algorithm for automating the breathing assistance with the Exo-Abs, by (i) solving a linear matrix inequality formulation of the Lyapunov stability condition against sector-bounded uncertainty and interindividual variability in the mechanics of the abdomen and the lungs and (ii) augmenting it with a heuristic yet effective gain scheduling algorithm. Using in silico evaluation based on realistic and plausible virtual patients, we demonstrated the efficacy and robustness of the automated breathing assistance of the Exo-Abs under a wide range of variability in spontaneous breathing and Exo-Abs efficiency: the absolutely stabilizing gain-scheduled proportional-integral control resulted in small exhalation trajectory tracking error (<30 ml) with smooth actuation, which was superior to (i) its proportional-integral control counterpart in tracking efficacy and to (ii) its proportional-integral-derivative control counterpart in chattering.