A centralized multi-objective model predictive control for a biventricular assist device: An in silico evaluation

• Main Authors: Koh, Vivian Ci Ai, Ho, Yong Kuen, Stevens, Michael Charles, Ng, Boon Chiang, Salamonsen, Robert Francis, Lovell, N.H., Lim, Einly
• Format: Article
• Published: Elsevier 2019
• Subjects: R Medicine
• Online Access: http://eprints.um.edu.my/20045/; https://doi.org/10.1016/j.bspc.2018.10.021

Speed regulation of dual left ventricular assist devices (LVADs) as a biventricular assist device (BiVAD) may be complicated by process interactions in a cardiovascular-biventricular assist device (CVS-BiVAD) environment. In this work, a conventional centralized model predictive control (MPC) algorithm that could handle process interactions in a multivariable control problem was modified to cater for the state and time-varying factors of the CVS-BiVAD system as well as to include multiple control objectives. Referred to as the centralized multi-objective model predictive control (CMO-MPC), the scheme's control objectives aim to: a) adapt pump flow rate according to the approximate Frank-Starling (FS) mechanism, b) avoid ventricular suction, and c) avoid vascular congestion. The control performance of the CMO-MPC was benchmarked with two non-centralized control schemes: the constant-speed (CS) control and the standard Frank-Starling like proportional-integral (PI-FS) control under two patient scenarios: exercise and postural change. Simulation results revealed that the CMO-MPC avoided suction and congestion in both patient scenarios as compared to the CS control and the PI-FS control, based on the assumptions made on risks of suction and congestion events. It is therefore proposed that the CMO-MPC should be a safe physiological controller for dual LVADs in the future when reliable pressure and flow sensors become clinically available.