Synergy of first principles modelling with predictive control for a biventricular assist device: in silico evaluation study

Control for dual rotary left ventricular assist devices (LVADs) used as a biventricular assist device (BiVAD) is challenging. If the control system fails, flow imbalance between the systemic and the pulmonary circulations would result, subsequently leading to ventricular suction or pulmonary congestion. With the expectation that advanced control approaches such as model predictive control could address the challenges naturally and effectively, we developed a synergistic first principles model predictive controller (MPC) for the BiVAD. The internal model of the MPC is a simplified state-space model that has been developed and validated in a previous study. A single Frank-Starling (FS) control curve was used to define the target pump flow corresponding to the preload on each side of the heart. The MPC was evaluated in a validated numerical model using three clinical scenarios: blood loss, myocardial recovery, and exercise. Simulation results showed that the MPC was effective in adapting to changes in physiological states without causing ventricular suction or pulmonary congestion. The use of MPC for a BiVAD eliminates the need for two controllers of dual LVADs thus making the task of controller tuning easier.