Indirect adaptive nonlinear control of drug delivery systems

Author

Polycarpou, Marios M. ; Conway, John Y.

Author_Institution

Dept. of Electr. & Comput. Eng., Cincinnati Univ., OH, USA

Volume

43

Issue

6

fYear

1998

fDate

6/1/1998 12:00:00 AM

Firstpage

849

Lastpage

856

Abstract

This paper investigates the use of adaptive neural network techniques for modeling and automatic control of mean arterial pressure through the intravenous infusion of sodium nitroprusside. An indirect model reference-based adaptive nonlinear control scheme with neural networks approximating the unknown nonlinearities, is developed. In this formulation nonlinear estimators are used to adaptively approximate the system uncertainty and augment the linear control law for improved performance. The overall design is based on self-tuning the controller to the specific response characteristics of individual patients. Computer simulations illustrate the ability of radial basis function networks to model the unknown nonlinearities and improve the closed-loop system characteristics

Keywords

adaptive control; biocontrol; closed loop systems; control nonlinearities; feedforward neural nets; intelligent control; model reference adaptive control systems; neurocontrollers; nonlinear control systems; patient treatment; physiological models; blood pressure control; closed-loop system; drug delivery systems; intelligent control; intravenous infusion; mean arterial pressure; model reference adaptive control; nonlinear control; nonlinearities; radial basis function networks; self-tuning; sodium nitroprusside; Adaptive control; Adaptive systems; Automatic control; Blood pressure; Control nonlinearities; Control systems; Drug delivery; Neural networks; Nonlinear control systems; Programmable control;

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/9.679024

Filename

679024