How to control if even experts are not sure: Robust fuzzy control

Abstract

In real life, the degrees of certainty that correspond to one of the same expert can differ drastically, and fuzzy control algoirthms translate these different degrees of uncertainty into different control strategies. In such situation, it is reasonable to choose a fuzzy control methodology that is the least vulnerable to this kind of uncertainty. We show that this "robustness" demand leads to min and max for &- and \\Gammaoperations, to 1 - x for negation, and to centroid as a defuzzification procedure.