Electrical Engineering amp; Computer Science Faculty Publications
Electrical Engineering amp; Computer Science
A Stable Self-Tuning Fuzzy Logic Control System for Industrial Temperature Regulation
Cleveland State University, Z.GAO@csuohio.edu
Multi-Dimensional Imaging, trautzsch@mdivac.com
Setech Inc.,, dawson@setechusa.com
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Zhiqiang, G., Trautzsch, T. A., amp; Dawson, J. G. (2002). A stable self-tuning fuzzy logic control system for industrial temperature regulation. IEEE Transactions on Industry Applications, 38, 2, 414-424.
Gao, Zhiqiang; Trautzsch, ftomas A.; and Dawson, James G., "A Stable Self-Tuning Fuzzy Logic Control System for Industrial Temperature Regulation" (2002). Electrical Engineering amp; Computer Science Faculty Publications. 105. https://engagedscholarship.csuohio.edu/enece_facpub/105
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A Stable Self-Tuning Fuzzy Logic Control System for Industrial Temperature Regulation
Zhiqiang Gao, Member, IEEE, Thomas A. Trautzsch, and James G. Dawson, Member, IEEE
Abstract—A closed-loop control system incorporating fuzzy logic has been developed for a class of industrial temperature control problems. A unique fuzzy logic controller (FLC) structure with an efficient realization and a small rule base that can be easily implemented in existing industrial controllers was proposed. The potential of FLC in both software simulation and hardware test in an industrial setting was demonstrated. This includes compensating for thermo mass changes in the system, dealing with unknown and variable delays, operating at very different temperature set points without retuning, etc. It is achieved by implementing, in the FLC, a classical control strategy and an adaptation mechanism to compensate for the dynamic changes in the system. The proposed FLC was applied to two different temperature processes and performance and robustness improve- ments were observed in both cases. Furthermore, the stability of the FLC is investigated and a safeguard is established.
Index Terms—Fuzzy logic, self-tuning, temperature control.
- INTRODUCTION
W
HILE MODERN control theory has made modest in roads into practice, fuzzy logic control has been rapidly gaining popularity among practicing engineers. This increased popularity can be attributed to the fact that fuzzy logic provides a powerful vehicle that allows engineers to incorporate human reasoning into the control algorithm. As opposed to the modern control theory, fuzzy logic design is not based on the mathemat ical model of the
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