Iterative Learning Control of a Left Ventricular Assist Device: Nonlinear Model Integration

Abstract Norm-optimal iterative learning control algorithms use plant models to predict the system behavior. In this paper, we focus on improving the performance in the norm-optimal iterative learning control of left ventricular assist devices. For this purpose, the previously used simple plant model is replaced by a piecewise linearized version of a nonlinear cardiovascular system model including the left ventricular assist device. Simulations are carried out to study the controller response to end-diastolic volume setpoint and preload changes. The results show minor improvements regarding the tracking performance and the rejection of disturbances but also an increase of computational effort compared to the previous algorithm.