Uncertainty Modeling with Interval Type-2 Fuzzy Logic Systems in Mobile Robotics

Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have been commonly attributed with the capability to model and cope with dynamic uncertainties. However, the interpretation of this uncertainty modeling using the IT2 FLSs have been rarely addressed or taken into consideration during the design of the respective fuzzy controller. This paper extends the previously proposed method for incorporating the experimentally measured input uncertainty into the design of the IT2 FLS. Two novel uncertainty quantifiers are proposed to track the uncertainty modeling throughout the inference process: the antecedent uncertainty and the consequent uncertainty quantifiers. Further, the new IT2 FLS design method was used to design a wall-following navigation controller for an autonomous mobile robot. It is demonstrated that the new IT2 FLS design offers improved uncertainty modeling, when compared to classical design methodologies. It was shown that the modeled input uncertainty is more accurately reflected in the system output as the geometry of the type-reduced interval centroid. This uncertainty model provides valuable information about the uncertainty associated with the output decision and can be used for more informer decision making.