In this paper, we study optimal in vitro realization of pulsatile coronary artery flows using newly proposed closed-loop feedback algorithms with a clearly defined mathematical performance objective, and we compare several flow control device technologies in terms of their ability to generate pulsatile flow signals as accurately as possible. In the literature, there are several published results for in vitro pulsatile flow realization systems. However, most of the proposed systems operate in an open-loop setting and use a single flow control device, and their performance is assessed mostly by graphical means without a clearly defined mathematical performance objective. Furthermore, some authors take the average of the generated pulsatile flow and compare it with the desired flow signal for performance analysis. What makes this work different from other published results in this area are: 1) the use of closed-loop feedback control rather than an open-loop approach; 2) the use of a clearly defined mathematical performance objective rather than a visual comparison of the generated and desired flow graphs; 3) newly proposed control algorithms with demonstrated performance improvements as compared to the published ones; 4) the use of multiple flow control devices, their performance comparisons, and optimal technology selection; and 5) not taking the average of the generated flow signal over several cycles for performance assessment, but instead using the nonaveraged actual flow signal and comparing it with the desired one. In pulsatile flow realization, what is important is the L-1 distance between the desired and delayed versions of the generated flows in the steady state regime, and the defined mathematical performance objective is completely based on this point. Furthermore, there are 3 different flow control devices that are comparatively analyzed: the pneumatic valve, the servo valve, and an AC inverter driving a centrifugal pump. Finally, there are several new closed-loop control algorithms proposed in this paper: P-Sigma, P-Sigma predictive, P-Sigma look ahead, and model-based nonlinear feed-forward, feed-forward predictive, and feed-forward look-ahead control. Each algorithm's performance is compared with that of PID performance as a benchmark test to demonstrate performance improvements. By proper selection of the flow control device, and optimal selection of the control algorithm and its parameters, we were able to achieve up to 75% reduction in error.