POSITION CONTROL OF DC MOTOR USING FRACTIONAL FUZZY INFERENCE SYSTEM (FFIS)


Cengiz M., Deniz F. N.

14th INTERNATIONAL CONFERENCE ON ENGINEERING & NATURAL SCIENCES, Sivas, Türkiye, 18 - 19 Temmuz 2022, ss.628-639

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Basıldığı Şehir: Sivas
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.628-639
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

Abstract

In fuzzy logic applications, a Fuzzy Inference System (FIS), which gives outputs for inputs by using various membership functions, plays an important role. The accuracy of the outputs of the FIS correlates with the use of inference methods as well as the rules suggested as an expert experience. In this direction, it is an important contribution to evaluate the FIS from different perspectives to get better results. In a former study, Fractional Fuzzy Inference System (FFIS) was suggested as an approach that claims to make a more meaningful evaluation using the traditional fuzzy inference system. Unlike the traditional FIS, fractional membership functions which consist of constant or dynamic fractional indices, are used in FFIS. In addition to the accuracy of the membership function, FFIS operates the evaluation process by considering the volume of the information depending on the accuracy degree. For this purpose, a horizontal membership degree is used together with the vertical membership degree used in the traditional fuzzy logic system. This horizontal membership function is defined in terms of fractional indices and vertical membership functions. The volume of the information is taken into account by using both vertical and horizontal membership functions. Fractional indices are values that range from 0 to 1. When it takes the value of 1, the traditional FIS is obtained. The FFIS, which uses the degree of accuracy of the information together with the volume of information, is an expansion of the traditional FIS. In this study, FFIS is used for position control of a DC motor. The results obtained with FFIS were compared with the results obtained with traditional FIS. It has been shown that the application with FFIS by selecting different fractional indices provides fine tuning. As a result, more satisfactory results were obtained with the use of FFIS.