This project examines defuzzification techniques to improve control performance in rotary kilns, suggesting maxima-based methods may enhance system stability.
Controlling nonlinear thermal systems like rotary cement kilns is a long-standing problem in industrial automation since they are dynamic and hard to predict. Fuzzy Logic Controllers (FLCs) present a strong alternative; nonetheless, the essential defuzzification phase - responsible for transforming fuzzy outputs into actionable values - has been inadequately examined in comparative analyses. This project creates a Mamdani-type FLC with 88 rules based on experts and four main inputs: combustion temperature, furnace torque, CO percentage, and preheater temperature. It will be used to control a high-capacity rotary kiln. We comprehensively examine five defuzzification methods: centroid, bisector, Smallest of Maximum (SOM), Middle of Maximum (MOM), and Largest of Maximum (LOM), using simulations generated in MATLAB's Fuzzy Logic Toolbox. The results reveal that the output is generally consistent among approaches, except for the kiln feed rate, which is quite sensitive. The research illustrates that the selection of defuzzification strategy significantly influences control performance, with maxima-based approaches providing enhanced stability. These results provide practical advice for developing strong fuzzy controllers for complicated industrial processes.
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