Error Estimation and Controllability Analysis of Contemporary Fuzzy Logic Control Models

Abstract

This research work is outgrowth of many contemporary intelligent control techniques and their implementation. But logically intelligent actions cannot control the conventional models without certain techniques of control, control algorithms, knowledge base models, mathematical models and also the predominant contemporary fuzzy techniques which give us best results under all the factors like complexity, non linearity, parameters varying with time as well as dynamics interactions among all the parameters. This thesis work highlights the need for managing intensive computations through modern upcoming technologies of artificial intelligence in the industry oriented problems. This work presents their association with new contemporary models. The purpose is to understand and explicate the interaction between advance fuzzy logic technologies and their impact on different uncertainties existing in industrial control systems/models. The concept of Fuzzy Logic (FL) was conceived by Lofti Zadeh, is a problem solving industrial control system methodology that lends itself to implementation. Fuzzy set theory provides a mathematical setting for the integration of subjective categories represented by membership functions of all the parameters concerning that control activity. Fuzzy set theory and its contemporary theories are an extension of classical set theory where elements of a set have grades of membership ranging from zero for non-membership to one for full membership. Exactly as for classical sets, there exist operators, relations, and mappings appropriate for these fuzzy sets. Fuzzy set theory is established as a theoretical basis for ordination. The notion central to fuzzy systems is that truth-values in fuzzy logic type -1 or membership values in fuzzy sets are indicated by a value on the range [0.0, 1.0] with 0.0 representing absolute falseness and 1.0 representing absolute truth. Type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-base fuzzy logic systems.