Masters Theses

Abstract

"Three iterative improvement algorithms are presented for the determination of process controller gains. An offline algorithm is developed and tested as a basis for comparison, and a simple on-line algorithm is developed as an incremental step toward the final algorithm, proportional on-line iterative improvement. The algorithms are based on an Artificial Neural Network learning method, and this method is compared with other control optimization techniques. The performance of each of the algorithms was experimentally evaluated in numerous realistically simulated process control situations consisting of flow, level, and temperature control loops with various values of dead-time and process noise. The experimental results reveal that the learned process controller gains behave in a predictable and intuitive manner.

The final algorithm performed very well on most of the simulated processes, but it performed only marginally well on processes where both dead-time and noise were present in significant quantities. The algorithm's complexity and memory requirements do not preclude its application in microprocessor-based single and multiple loop controllers"--Abstract, page iii.

Advisor(s)

Wilkerson, Ralph W.

Committee Member(s)

Ho, C. Y. (Chung You), 1933-1988
Dagli, Cihan H., 1949-

Department(s)

Computer Science

Degree Name

M.S. in Computer Science

Comments

A report which is substantially this thesis is available here for download.

Publisher

University of Missouri--Rolla

Publication Date

Fall 1992

Pagination

ix, 84 pages

Note about bibliography

Includes bibliographical references (pages 82-83).

Rights

© 1992 Ryan George Rosandich, All rights reserved.

Document Type

Thesis - Restricted Access

File Type

text

Language

English

Thesis Number

T 6520

Print OCLC #

27864566

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