This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.
This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.
The dc-dc converters are highly efficient tools used to supply power to different systems, they have a nonlinear behavior and variations at their main parameters could affect their stability. This document studies and compares different control strategies, linear and non linear controllers applied to a Buck converter.There are mainly three control strategies treated in this paper. First an optimal control based design, by employing The Quadratic Performance Index (QPI) is used, second a knowledge based fuzzy control is studied and third an Artificial Neural Network (ANN) as a dynamic emulator of the fuzzy control is proposed. Some comparisons about the systems composed by the plant and a controller, in variation of a few plant parameters were made; in addition the computational time in simulation is compared between the two intelligent controllers.
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