Page 63 - IJOCTA-15-3

An International Journal of Optimization and Control: Theories & Applications
ISSN: 2146-0957 eISSN: 2146-5703
Vol.15, No.3, pp.435-448 (2025)
https://doi.org/10.36922/IJOCTA025070023

RESEARCH ARTICLE

FPGA design and implementation of fuzzy learning control:
Application on DC motor position control

1
1
1
Mohand Achour Touat , Hocine Khati , Arezki Fekik 2,3 , Ahmad Taher Azar 4,5 , Hand Talem ,
1
Rabah Mellah and Saim Ahmed 4,5*
1
Design and Drive of Production Systems Laboratory, Faculty of Electrical and Computing Engineering,
University Mouloud Mammeri of Tizi-ouzou, Tizi-ouzou, Algeria
2
Nantes University, Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France
3
Department of Electrical Engineering Faculty of Applied Sciences, University of Bouira, Bouira, Algeria
4
College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
5
Automated Systems and Computing Lab (ASCL), Prince Sultan University, Riyadh, Saudi Arabia
Mohand-achour.touat@ummto.dz, hocine.khati@ummto.dz, Arezki.Fekik@univ-nantes.fr, aazar@psu.edu.sa,
talemhand2015@gmail.com, Rabah.mellah@ummto.dz, sahmed@psu.edu.sa
ARTICLE INFO ABSTRACT
Article History:
Received: February 10, 2025 This paper investigates the implementation of a Fuzzy Model Reference Learn-
Revised: March 12, 2025 ing Control (FMRLC) on a Zedboard Zynq-7000 FPGA. The proposed adap-
Accepted: March 27, 2025 tive controller dynamically adjusts its knowledge base and incorporates a
Published Online: May 8, 2025 memory-based control mechanism to retain and utilize past results in recurring
situations. The design and deployment of the controller were carried out us-
Keywords: ing the MATLAB/Simulink environment and applied to the angular position
Learning fuzzy control control of a DC motor. Initially, the controller was tested using the FPGA-
FPGA In-the-Loop (FIL) approach to assess its robustness against disturbances in
HDL Coder simulation. Subsequently, it was experimentally validated for real-time motor
Adaptive control position control. The results obtained in FIL simulations and experimental
FIL tests demonstrate high tracking accuracy and strong disturbance rejection.
Optimization These findings underscore both the superiority of the proposed controller over
AMS Classification 2010: the conventional PID controller and the effectiveness of the adopted design
26A33; 34A08; 35H15; 34K50 methodology.
47H10; 60H10

1. Introduction knowledge in control systems. Widely adopted
in automatic control, fuzzy logic avoids intri-
In recent years, fuzzy logic applications have ex- cate nonlinear equations while leveraging expert
perienced significant growth across various do- knowledge. However, experts cannot always an-
mains, ranging from industrial applications such ticipate uncertainties arising during system oper-
as medical instruments and robotics to consumer ation. To ensure safe system functionality and ac-
products like washing machines, cameras, and au- curate trajectory tracking despite disturbances, it
tomobiles. Fuzzy logic, based on fuzzy set theory, is crucial to automatically update human knowl-
offers a set of if-then rules, eliminating the need edge without operator intervention.
for complex differential equations. This approach To address these challenges, various adaptive
provides a structured methodology for modeling, control algorithms have been introduced in the
manipulating, and implementing human heuristic literature, including fuzzy adaptive control. 1–4
*Corresponding Author
435