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.


Computer Science


This report is substantially the M.S. thesis of the first author, completed Fall 1992.

Report Number


Document Type

Technical Report

Document Version

Final Version

File Type





© 1992 University of Missouri--Rolla, All rights reserved.

Publication Date

Fall 1992