In this article, we propose a robust model predictive control (MPC) approach for pressure-controlled ventilation, with the goal to increase the safety of the patient by introducing safety constraints to the controller on a physiological basis. For the theoretical guaranties of MPC to hold in practice, the model must represent the reality sufficiently well. Yet physiological lung models of individual patients are not readily available, and parameters need to be estimated from pressure and flow data at the patient’s airways. In this article, the estimation uncertainty as well as modelling errors are considered as disturbances to the system against which an MPC is robustified. By using an auxiliary control law together with the MPC, it is possible to confine the state error to a closed set around the trajectory of a nominal system, allowing to guarantee constraint satisfaction in the presences of (bounded) disturbances.
|Number of pages||2|
|Publication status||Published - 01.02.2020|
|Event||AUTOMED - Automation in Medical Engineering 2020 - Lübeck, Germany|
Duration: 02.03.2020 → 03.03.2020
|Conference||AUTOMED - Automation in Medical Engineering 2020|
|Period||02.03.20 → 03.03.20|