Design of a self-tuning rule based controller for a gasoline refinery catalytic reformer

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