Abstract
Design concepts for self-tuning knowledge-based controllers are studied. To accomplish this, two interacting rule-based controllers are constructed for supervisory control and system optimization of a gasoline catalytic reformer. The knowledge bases incorporate 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 coefficient 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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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