DC Motor Friction Identification With ANFIS and LS-SVM Method / Muhammad Zaiyad Ismail ... [et al.]

Ismail, Muhammad Zaiyad and Azizan, Nur Akmal and Ja’afar, Rabi’atul’adawiyah and Ayub, Muhammad Azmi and Khalid, Noor Khafifah (2017) DC Motor Friction Identification With ANFIS and LS-SVM Method / Muhammad Zaiyad Ismail ... [et al.]. Journal of Mechanical Engineering (JMechE), SI 4 (5). pp. 98-108. ISSN 18235514 (Unpublished)


Friction has been an old age problem for any motion system to accomplish its optimum performance. Friction compensation has been identified as an effective strategy to enhance the performance of a motion system. To be able to compensate the friction in motors, the friction itself needs to be identified. Through the latest development in Artificial Intelligent, it has been obvious that the major Artificial Intelligent-paradigms are able to resemble any nonlinear functions precisely and hence, being used as one approach in friction modeling and identification. In this paper, a DC motor is selected as the representative of simple motor. A real-time experiment involving a DC motor is required in getting the best velocity to friction torque relationship. By using MatLab, the friction modeling data is trained with two different methods, which are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Least Squares Support Vector Machine (LS-SVM). The performance of both methods is compared and analysed.


Item Type: Article
Email / ID Num.
Ismail, Muhammad Zaiyad
Azizan, Nur Akmal
Ja’afar, Rabi’atul’adawiyah
Ayub, Muhammad Azmi
Khalid, Noor Khafifah
Subjects: T Technology > TJ Mechanical engineering and machinery
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering (JMechE)
UiTM Journal Collections: UiTM Journal > Journal of Mechanical Engineering (JMechE)
ISSN: 18235514
Volume: SI 4
Number: 5
Page Range: pp. 98-108
Keywords: ANFIS; LS-SVM; DC Motor; Friction Modeling
Date: 2017
URI: https://ir.uitm.edu.my/id/eprint/39327
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