Experimental Studies on Automated Regulation of Hemodynamic Variables

Two different control methodologies are developed for automated regulation of hemodynamic variables. These controllers are designed to regulate mean arterial pressure (MAP) and cardiac output (CO) in critical care subjects using inotropic and vasoactive drugs. Both controllers account for inter– and intra–patient variability and handle drug infusion constraints. The first approach is a multiple model predictive controller (MMPC). The algorithm uses a multiple model adaptive approach in a model predictive control framework to account for variability and explicitly handle drug rate constraints. The second approach, a robust direct model reference adaptive controller (DMRAC) is developed for plants with uncertainty in both the time delay elements and in the transfer function coefficients, such as the drug infusion process. Further modifications are introduced to handle drug rate constraints. The controllers are experimentally evaluated on canines that are pharmacologically altered to exhibit symptoms of hypertension and depressed cardiac output.