Design of a self-tuning rule based controller for a gasoline refinery catalytic reformer

Document Type

Article

Publication Date

1-1-1990

Abstract

The objective of this paper is to explore design concepts for self-tuning knowledge based controllers. To accomplish this, two interacting rule based controllers for supervisory control and system optimization are constructed to control a gasoline catalytic reformer. The knowledge bases for the controllers are established from human operator experience and basic engineering knowledge about the process dynamics. Inference is provided by a fuzzy logic engine. After manual tuning of the controller scaling coefficients is accomplished, a crisp heuristic is developed for self tuning. The performance of the self-tuning controller is tested against perturbations of a simulation model of the catalytic reformer. © 1990 IEEE

Identifier

0025386094 (Scopus)

Publication Title

IEEE Transactions on Automatic Control

External Full Text Location

https://doi.org/10.1109/9.45171

e-ISSN

15582523

ISSN

00189286

First Page

156

Last Page

164

Issue

2

Volume

35

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