Direct Control Strategy using Polynomial Fuzzy-Based Adaptive Fractional Order PID Controller

Authors

Ali Rospawan, Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, TaiwanFollow
Clara Lavita Angelina, Department of Electronics Engineering, Politeknik Manufaktur Negeri Bangka Belitung, Sungailiat 33211, IndonesiaFollow
Faisal Samsuri, Department of Automation and Control, College of Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanFollow
Muhammad Yeza Baihaqi, Information Sciences Division, Nara Institute of Science and Technology, Nara Prefecture 630-0192, JapanFollow
Edmun Halawa, Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanFollow
Muhammad Munajat, Department of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 10607, TaiwanFollow
Vincent Vincent, School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, IndonesiaFollow
Surawan Setiyadi, Mechatronics Study Program, Astra Manufacturing Polytechnic, Cikarang 17530, IndonesiaFollow
Irwan Purnama, National Research and Innovation Agency, Bandung 40135, IndonesiaFollow
Joni Welman Simatupang, Electrical Engineering Study Program, Defense University, Bogor 16810, IndonesiaFollow

Abstract

This paper presents a novel direct control strategy using a polynomial fuzzy neural network-based adaptive fractional order proportional integral derivative (PFNN-AFOPID) controller for nonlinear and time-varying systems. The proposed approach integrates the enhanced flexibility of fractional order calculus PID with the superior nonlinear approximation capabilities of polynomial fuzzy models, enabling dynamic adjustment of all control parameters without requiring precise mathematical modeling of system dynamics. By extending traditional PID control with fractional-order operations, the controller achieves improved frequency response and robustness against disturbances. Experimental validation on a DC motor position control system demonstrates significant performance improvements. Compared to traditional PID, the proposed PFNN-AFOPID achieved a performance improvement of 53.69% in RMSE, 78.56% in ISE, 69.92% in IAE, and 83.98% in ITAE. When compared to the existing fuzzy neural network-based adaptive PID (FNN-APID), our approach delivered improvements of 21.06% in RMSE, 28.79% in ISE, 5.69% in IAE, and 32.86% in ITAE. These results confirm the superior capability of the proposed approach in handling system nonlinearities while maintaining precise control under varying operational conditions, without requiring prior system dynamics knowledge or extensive offline training.

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Recommended Citation

Rospawan, Ali; Angelina, Clara Lavita; Samsuri, Faisal; Baihaqi, Muhammad Yeza; Halawa, Edmun; Munajat, Muhammad; Vincent, Vincent; Setiyadi, Surawan; Purnama, Irwan; and Simatupang, Joni Welman (2025) "Direct Control Strategy using Polynomial Fuzzy-Based Adaptive Fractional Order PID Controller," Makara Journal of Technology: Vol. 29: Iss. 2, Article 3.
DOI: 10.7454/mst.v29i2.1723
Available at: https://scholarhub.ui.ac.id/mjt/vol29/iss2/3