BibTeX
@MISC{Shmukler06any-timefuzzy,
author = {Edward Shmukler},
title = {ANY-TIME FUZZY CONTROLLER},
year = {2006}
}
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Abstract
Fuzzy logic has been successfully applied in various fields. However, as fuzzy controllers increase in size and complexity, the number of control rules increases exponentially and real-time behavior becomes more difficult. This thesis introduces an any-time fuzzy controller. Much work has been done to optimize and speed up a controlling process, however none of the existing solutions provides an any-time behavior. This study first introduces several constraints that should be satisfied in order to guarantee an any-time behavior. These constraints are related to aggregation and defuzzification phases of fuzzy control. Popular aggregation and defuzzification methods (max-min, sum-product, MOM and COG) are first shown to satisfy these constraints, and then three linearization methods are presented. Linearization methods are used to reorder fuzzy rules base such that a reordered rule base would result in any-time behavior. Finally, several approximation methods are described, that do not break any-time behavior, while causing the intermediate result of an any-time controller to come closer to the final (full calculation) result in a shorter time. The exact influence and worthiness of approximation
Keyphrases
any-time behavior any-time fuzzy controller linearization method popular aggregation intermediate result several constraint fuzzy controller fuzzy control fuzzy rule controlling process defuzzification phase various field full calculation exact influence control rule reordered rule base much work several approximation method fuzzy logic defuzzification method any-time controller real-time behavior
