A model predictive control strategy was developed and tested on a nonlinear canine circulatory model for the regulation of hemodynamic variables under critical care conditions. Different patient conditions such as congestive heart failure, post-operative hypertension, and sepsis shock were studied in closed-loop simulations. The model predictive controller, which uses a different linear model depending on the patient condition, allowed constraints to be explicitly enforced. The controller was initially tuned on the basis of a linear plant model, then tested on the nonlinear physiological model; the simulations demonstrated the ability to handle constraints, such, as drug dosage specifications, commonly desired by critical care physicians.