Intravenous Drug Delivery System for Blood Pressure Patient Based on Adaptive Parameter Estimation

Abstract

Controlled drug delivery systems (DDS's) is an electromechanical system that supports the injection of a therapeutic drug intravenously into a patient's body and easily controls the infusion rate of patient's drug, blood pressure, and time of drug release. The controlled operation of mean arterial blood pressure (MABP) and cardiac output (CO) is highly desired in clinical operations. Different methods have been proposed for controlling MABP, all methods have certain disadvantages according to patient model. In this article, the authors propose blood pressure control using integral reinforcement learning based fuzzy inference systems (IRLFI) based on parameter estimation techniques and have compared this method in terms of integral squared error (ISE), integral absolute error (IAE), integral time-weighed absolute error (ITAE), root mean square error (RMSE), convergence time (CT).