Type-1 fuzzy logic algorithm for low cost embedded systems. (December 2020)

• Record Type: Journal Article
• Authors: Sobrinho, André Sanches Fonseca; Junior, Francisco Granziera
• Is Part Of: Computers & electrical engineering. Volume 88(2020)
• Publication Date: 2020-12
• Subjects: Algorithm -- Embedded systems -- Fuzzy logic -- Low cost microcontrollers; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106861
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A type-1 fuzzy logic algorithm developed in C language in order to be used in different applications. This algorithm is embedded in an 8-bit low cost microcontroller and it needs low amount of program and data (RAM) memories. This algorithm offers a great performance regarding fuzzy processing time and accuracy in the defuzzification process. Abstract: This paper presents in detail a type-1 fuzzy logic algorithm developed in C language to be used in 8-bit low cost embedded systems, adaptable to different applications. It is also shown a rigorous comparison between the proposed algorithm and type-1 fuzzy conventional algorithm, in which the accuracy, the processing speed and especially the low amount of memory necessary for the execution of the proposed code are highlighted. In order to verify the operation of the proposed work, the algorithm was developed for the PIC18 microcontrollers and the results were obtained by simulations with PROTEUS software and functional comparisons with MATLAB software. Graphical abstract: The type-1 fuzzy logic algorithm was developed in C language to be used in 8-bit low cost embedded systems and be easily adaptable to different applications. This algorithm presents better performance regarding accuracy, fuzzy processing time and required RAM memory compared with conventional algorithms. Image, graphical abstract