Adaptive fuzzy neural modeling and control scheme for mean arterial pressure regulation

This paper presents an adaptive modeling and control scheme for blood pressure regulation based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model the unknown nonlinearities of complex drug delivery systems and adapt to changes and uncertainties in these systems online. It offers salient features, such as dynamic fuzzy neural topology, fast online learning ability and adaptability, etc. System approximation formulated by the G-FNN is thus employed in the adaptive control of drug infusion for blood pressure regulation. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through the intravenous infusion of sodium nitroprusside (SNP), which is one of the most attractive applications in automation of drug delivery. Simulation study demonstrates superior performance of the proposed approach for estimating the drug´s effect and regulating blood pressure at a prescribed level.