Application of fuzzy logic methods to modeling of the process of controlling complex technical systems

In recent decades, the concept of “fuzzy” control has emerged in engineering practice, including machinery building, when a complex technical object is controlled in conditions of incomplete and / or insufficiently formalized information. Such systems are often characterized by insufficient reliability and degree of initial data formalization. One of the approaches to such objects is to use fuzzy logic methods. The first step of such processes modeling should include determination of input and output variables and their membership functions and creation of a knowledge base in the form of a set of “IF-THEN” rules. The Mathcad computing system is considered as a tool for modeling processes with fuzzy logic methods using the battery charging control process as an example. This paper presents detailed analysis of advantages and describes software implementation for the Mamdani algorithm. However the proposed approach can be used for other algorithms.

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Application of fuzzy logic methods to modeling of the process of controlling complex technical systems

Semantic Scholar · Engineering · 2019

Abstract

In recent decades, the concept of “fuzzy” control has emerged in engineering practice, including machinery building, when a complex technical object is controlled in conditions of incomplete and / or insufficiently formalized information. Such systems are often characterized by insufficient reliability and degree of initial data formalization. One of the approaches to such objects is to use fuzzy logic methods. The first step of such processes modeling should include determination of input and output variables and their membership functions and creation of a knowledge base in the form of a set of “IF-THEN” rules. The Mathcad computing system is considered as a tool for modeling processes with fuzzy logic methods using the battery charging control process as an example. This paper presents detailed analysis of advantages and describes software implementation for the Mamdani algorithm. However the proposed approach can be used for other algorithms.

References (13)

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12Fuzzification, which involves determination of input parameters when crisp inputs having their own group of membership functions are specified

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