An intelligent adaptive control scheme for postsurgical blood pressure regulation.
This paper presents an adaptive modeling and control scheme for drug delivery systems based on a generalized fuzzy neural network (G-FNN). The proposed G-FNN is a novel intelligent modeling tool, which can model 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. System approximation formulated by the G-FNN is employed in the adaptive controller design for drug infusion in intensive care environment. In particular, this paper investigates automated regulation of mean arterial pressure (MAP) through intravenous infusion of sodium nitroprusside (SNP), which is one attractive application in automation of drug delivery. Simulation studies demonstrate the capability of the proposed approach in estimating the drug's effect and regulating blood pressure at a prescribed level.
Gao
Yang
Y
School of Electronics and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK. yang.gao@surrey.ac.uk
Er
Meng Joo
MJ
D001185:Artificial Intelligence; D001794:Blood Pressure / Q000187:drug effects / Q000502:physiology; D016503:Drug Delivery Systems / Q000379:methods; D017143:Fuzzy Logic; D009599:Nitroprusside / Q000494:pharmacology; D011184:Postoperative Period
D009599:Nitroprusside