AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025070023
Submit to IJOCTA
Cite this article
21
Download
51
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
RESEARCH ARTICLE

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

Mohand Achour Touat1 ,  Hocine Khati1 ,  Arezki Fekik2,3 ,  Ahmad Taher Azar4,5 ,  Hand Talem1 ,  Rabah Mellah1 ,  Saim Ahmed4,5*
Show Less
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
IJOCTA, 025070023 https://doi.org/10.36922/IJOCTA025070023
Received: 10 February 2025 | Revised: 12 March 2025 | Accepted: 27 March 2025 | Published online: 8 May 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

This paper investigates the implementation of a Fuzzy Model Reference Learning Control (FMRLC) on a Zedboard Zynq-7000 FPGA. The proposed adaptive controller dynamically adjusts its knowledge base and incorporates a memory-based control mechanism to retain and utilize past results in recurring situations. The design and deployment of the controller were carried out using the MATLAB/Simulink environment and applied to the angular position control of a DC motor. Initially, the controller was tested using the FPGA-In-the-Loop (FIL) approach to assess its robustness against disturbances in simulation. Subsequently, it was experimentally validated for real-time motor position control. The results obtained in FIL simulations and experimental tests demonstrate high tracking accuracy and strong disturbance rejection. These findings underscore both the superiority of the proposed controller over the conventional PID controller and the effectiveness of the adopted design methodology.

Keywords
Learning fuzzy control
FPGA
HDL Coder
Adaptive control
FIL
Optimization
Funding
This paper is funded by Prince Sultan University, Riyadh, Saudi Arabia. The authors would like to thank Prince Sultan University for paying the article processing fee for this paper.
Conflict of interest
The authors declare that have no conflict of interest.
References
  1. Kemal U, Beyza Nur A. Adaptive MIMO fuzzy PID controller based on peak observer. Int J Optim Control: Theor Appl. 2023;13.

 

  1. Masjudin, Alimuddin, Aisah SN, Wiryadinata R. Dc motor speed control based on fuzzy adaptive with fuzzy model reference learning control (fm- rlc) algorithm. In: 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE). IEEE ; 2020: 79-83.

 

  1. Devashish J, Arifa A, Sanatan K, Debanjan R. Fuzzy-PID and interpolation: a novel synergetic approach to process control. Int J Optim Control: Theor Appl. 2024;14(4):355-364.

 

  1. Fajrianto MR, Wahyudi W, Sudjadi S. Peran- cangan kontroler fuzzy model reference learning control (fmrlc) berbasis mikrokontroler atmega16 sebagai kendali motor brushless dc (bldc). Transient: J Ilmiah Teknik Elektro2017;6:597–604.

 

  1. Saim A, Haoping W, Yang T. Fault tolerant control using fractional-order terminal sliding mode control for robotic manipulators. Inform. Control. 2018;27(1):55-64.

 

  1. Saim A, Ahmad Taher A, Ibraheem KI. Nonlinear system controlled using novel adaptive fixed-time SMC. AIMS Math 2024;9(4):7895-7916.

 

  1. Kopasakis G, Kopasakis G. Adaptive perfor- mance seeking control using fuzzy model reference learning control and positive gradient control. In: 33rd Joint Propulsion Conference and Exhibit ; 1997: 3191.

 

  1. Zhen L, Xu L. Fuzzy learning enhanced speed control of an indirect field-oriented induction ma- chine drive. IEEE Trans Control syst Technol. 2000;8:270–278.

 

  1. Mayhan P, Washington G. Fuzzy model reference learning control: a new control paradigm for smart structures. Smart Mater struct. 1998;7:874.

 

  1. Layne JR, Passino KM, Yurkovich S. Fuzzy learning control for antiskid braking systems. IEEE Trans Control Syst Technol. 1993;1:122–129.

 

  1. Reay D, Dunnigan M. Learning issues in model reference based fuzzy control. IEEE Proc Control Theor Appl., 1997;144:605–611.

 

  1. Duka AV, Oltean SE, Dulau M. Model reference adaptive control and fuzzy model reference learn- ing control for the inverted pendulum. comparative analysis. In: Proceedings of WSEAS Inter- national Conference on Dynamical Systems and Control ; 2005:168–173.

 

  1. Monmasson E, Idkhajine L, Cirstea MN, Bahri I, Tisan A, Naouar MW. Fpgas in industrial control applications. IEEE Trans Ind Informat; 2011;7:224-243.

 

  1. Hace A, Franc M. Fpga implementation of sliding-mode control algorithm for scaled bilateral teleoperation. IEEE Transac Ind Inform. 2012;9:1291–1300.

 

  1. Fekik A, Khati H, Azar AT, et al. FPGA in the loop implementation of the PUMA 560 robot based on backstepping control. IET Control Theor Appl. 2024;18(15): 1877-1891.

 

  1. Huerta-Moro S, Taviz´on-Aldama JD, Tlelo- Cuautle E. FPGA implementation of sliding mode control and proportional-integral-derivative controllers for a DC–DC buck converter. 2024;12(10):184.

 

  1. Wang J, Li M, Jiang W, Huang Y, Lin R. A design of FPGA-based neural network PID controller for motion control system. 2022;22(3):889.

 

  1. Li Y, Li SE, Jia X, Zeng S, Wang Y. FPGA accelerated model predictive control for autonomous J Intell Connect Vehicl. 2022;5(2):63-71

 

  1. Meghwal R, Yadav VK, Vardia M. Robust fuzzy controller design with FPGA implementation for matrix converter based induction motor drive. e-Prime Adv Elect Eng Electron Energy 2024;10:100752.

 

  1. El Attafi A, El Alami H, Bossoufi B, et al. Robust control of a wind energy conversion system: FPGA real-time implementation. 2024; 10(15):e35712.

 

  1. Chakravarty S. Technology and Engineering Applications of Simulink. BoD–Books on Demand ;

 

  1. Akkaya S¸, U¨ zgu¨n HD, Akbati O. Fuzzy logic controller implementation with fpga in the loop simulation. In: Proceedings of the 2017 International Conference on Mechatronics Systems and Control Engineering ; 2017: 33-37.

 

  1. Akbatı O, U¨ zgu¨n HD, Akkaya S. Hardware- in-the-loop simulation and implementation of a fuzzy logic controller with fpga: case study of a magnetic levitation system. Trans Instit Measure Control., 2019;41:2150-2159.

 

  1. Deliparaschos K, Nenedakis F, Tzafestas SG. Design and implementation of a fast digital fuzzy logic controller using fpga technology. J Intell Ro- bot Syst., 2006;45:77-96.

 

  1. Khati H, Mellah R, Talem H. Neuro-fuzzy control of a position-position teleoperation system using fpga, In: 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE ; 2019:64-69.

 

  1. Khati H, Talem H, Mellah R, Bilek A. Neuro- fuzzy control of bilateral teleoperation system using fpga. Iranian J Fuzzy Syst. 2019;16:17-32.

 

  1. Azzouz B, Hadjira B. Hardware/software code- sign for intelligent motor drive on an fpga, In: 2020 2nd International Workshop on Human- Centric Smart Environments for Health and Well-being (IHSH). IEEE ; 2021; 227-232.

 

  1. Lotfy A, Kaveh M, Mosavi M, Rahmati A. An enhanced fuzzy controller based on improved genetic algorithm for speed control of dc motors. Analog Integr Circ Signal Process. 2020;105:141-155.

 

  1. Abdelkrim H, Othman SB, Saoud SB. Fpga implementation of self-reconfigurable fuzzy logic controller. In: 2018 International Conference on Advanced Systems and Electric Technologies(IC ASET). IEEE ; 2018:151-156.

 

  1. Moussa I, Khedher A. Real-time wte using flc implementation on fpga board: theoretical and experimental studies. In: 2020 17th International Multi-Conference on Systems, Signals & Devices(SSD). IEEE ; 2020: 428-433.

 

  1. Anand, M.S., Tyagi, B. (2012). Design and implementation of fuzzy controller on fpga. Int J Intell Syst Applicat. 2012; 10:35-42.

 

  1. Layne JR, Passino KM. Fuzzy model reference learning control. J Intell Fuzzy Syst. 1996;4:33–47.

 

  1. Ljung L. System Identification Tool-box: User’s Guide. Citeseer; 1995.

 

  1. Finnerty A, Ratigner H. Reduce power and cost by converting from floating point to fixed point. In: WP491 (v1. 0) ; 2017: 9-10.

 

  1. Crockett LH, Elliot RA, Enderwitz MA. The Zynq Book Tutorials for Zybo and Zedboard. Strathclyde Academic Media; 2015.

 

  1. Khati H, Fekik A, Azar AT, et al. Optimizing UAV stability and control with FPGA-based PID control system design. In: 2024 International Conference on Control, Automation and Diagno- sis (ICCAD). IEEE ;2024:1-6.

 

  1. Fekik A, Khati H, Azar AT, Hamida ML, De- noun H, & Kamal NA. FPGA-based performance evaluation of backstepping control and computed torque control for industrial robots. Int J Automat Control. 2025;19(1):101-132.
Next article in this issue
Share
Back to top
An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept