Does a multiple-model adaptive predictive controller improve the regulation of mean arterial pressure and cardiac output in animal models of congestive heart failure?
A multiple-model adaptive predictive controller robustly regulated mean arterial pressure and cardiac output in animal models of congestive heart failure.
A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.
Yu et al. (Wed,) studied this question.