Design of Fuzzy Logic Controller with Inconsistent Rule Base

Abstract

As major functional units, the fuzzy logic controller (FLC) includes a fuzzy rule base and an inference engine. In constructing a fuzzy rule base, however, uncertainties and imprecision in the information about the controlled plant or in the extracted knowledge about actions of operators may result in inconsistent rules. But conventional inference methods for FLC often fail to handle such inconsistencies. In this article is proposed an effective method for obtaining a final conclusion from such inconsistent if-then rules. Also, as an alternative to conventional methods, a “minimum distance inference method” is applied for FLC. In the method, a metric is introduced to represent the distance between two fuzzy sets, and a new measure of certainty is used as a weight in the optimization to find a conclusion fuzzy set. The usefulness of the proposed methodologies is shown via a computer simulation of controlling a realistic overhead crane system.