Fuzzy Logic Speed Regulator for D.C. Motor Tuning

Authors

  • Wasim Raza Department of Mathematics, University of Sargodha
  • Dieky Adzikya Department of Mathematics, Institut Teknologi Sepuluh Nopember, Surabaya
  • Saba Mehmood Department of Mathematics. Thal University Bhakkar
  • Syeda Rabbia Wasti Institute of Southern Punjab, Multan
  • Muhammad Jafar Hussain Institute of Southern Punjab, Multan
  • Aftab Ahmad Institute of Southern Punjab, Multan
  • Muhammad Talha Usman Institute of Southern Punjab, Multan
  • Sajid Raza Institute of Southern Punjab, Multan

DOI:

https://doi.org/10.31764/jtam.v8i1.16919

Keywords:

D.C. motor, PID Controller, Fuzzy Logic Controller, Mamdani, SIMULINK.

Abstract

A D.C. motor's rotational speed is regulated in this study using a PID controller and a fuzzy logic controller. In contrast to the fuzzy logic controller, which uses rules based on knowledge and experience, the proportional-integral-derivative (PID) controller requires a mathematical system model.   This study investigates the regulation of a DC motor's velocity using PID and fuzzy logic controllers. The PID controller utilizes a mathematical model and parameter tuning by trial and error. Still, the fuzzy logic controller (FLC) operates on rule-based knowledge, enabling it to handle the nonlinear features of the DC motor effectively. The FLC design entails intricate determinations, including the establishment of a rule base and the process of fuzzification. A total of 49 fuzzy rules have been devised to achieve precise control. Based on MATLAB/SIMULINK simulations, the study concludes that the Fuzzy Logic Controller (FLC) beats the Proportional-Integral-Derivative (PID) controller. The FLC exhibits superior transient and steady-state responses, shorter response times, reduced steady-state errors, and higher precision. This study emphasizes the efficacy of the FLC (Fuzzy Logic Controller) in dealing with the difficulties associated with DC motor control. It presents a strong argument for the suitability and efficiency of FLCs in industrial environments compared to conventional PID (Proportional-Integral-Derivative) controllers. There are a wide variety of ways to construct a fuzzy logic controller. The speed error and the rate of change in the speed error are two inputs to the FLC. Defuzzification is done by focusing on the core of the problem. The results show that FLC is superior to PID controllers in efficiency and effectiveness due to its reduced transient and steady-state factors.

Author Biography

Wasim Raza, Department of Mathematics, University of Sargodha

Lecturer, Department of Mathematics, University of Sargodha, Bhakkar.

References

Al-Khaykan, A., Alrubaie, A. J., Abed, A. M., Kadhim, S. A., & Alyasiri, Y. I. (2023). New methodology to control speed of DC motor. 050001. https://doi.org/10.1063/5.0163321

Almatheel, Y. A., & Abdelrahman, A. (2017). Speed control of DC motor using Fuzzy Logic Controller. 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), 1–8. https://doi.org/10.1109/ICCCCEE.2017.7867673

Astrom, K. J. (2002). Control System Design: Chapter 5 - Feedback Fundamentals. Control System Design, 1(1).

Benson OGUNDARE, A. (2013). Validation of D.C Motor Speed Control Model Using PIC 16F628A Microcontroller. International Journal of Engineering Research & Technology (IJERT), 2(8).

El-Shimy, M. E., & Zaid, S. A. (2016). Fuzzy PID controller for fast direct torque control of induction motor drives. Journal of Electrical Systems, 12(4).

García-Martínez, J. R., Cruz-Miguel, E. E., Carrillo-Serrano, R. V., Mendoza-Mondragón, F., Toledano-Ayala, M., & Rodríguez-Reséndiz, J. (2020). A PID-type fuzzy logic controller-based approach for motion control applications. Sensors (Switzerland), 20(18). https://doi.org/10.3390/s20185323

Hambley, A. R. (2011). Electrical Engineering Principles and Applications. Electrical Engineering Principles and Applications.

Hameed, W. I. (2012). Speed Control of Separately Excited DC Motor using Fuzzy Neural Model Reference Controller. International Journal of Instrumentation and Control Systems, 2(4). https://doi.org/10.5121/ijics.2012.2403

Hilal, M., ALRikabi, H., & A. Aljazaery, I. (2023). A Control System of DC Motor Speed: Systematic Review. Wasit Journal of Computer and Mathematics Science, 2(1), 93–111. https://doi.org/10.31185/wjcm.121

Jang, J. S. R., & Gulley, N. (2015). Fuzzy logic toolbox user’s guide. The Mathworks Inc, 1(995).

Kaur, M., & Patel, J. K. B. (2019). Control of direct current motor using intelligent PID controller. Journal of Advanced Research in Dynamical and Control Systems, 11(1 Special Issue).

Li, H., Song, B., Chen, T., Xie, Y., & Zhou, X. (2021). Adaptive fuzzy PI controller for permanent magnet synchronous motor drive based on predictive functional control. Journal of the Franklin Institute, 358(15). https://doi.org/10.1016/j.jfranklin.2021.07.024

Maarif, A., & Setiawan, N. R. (2021). Control of dc motor using integral state feedback and comparison with pid: Simulation and arduino implementation. Journal of Robotics and Control (JRC), 2(5). https://doi.org/10.18196/jrc.25122

Mahmud, M., Motakabber, S. M. A., Alam, A. H. M. Z., & Nordin, A. N. (2020). Control BLDC motor speed using PID controller. International Journal of Advanced Computer Science and Applications, 11(3). https://doi.org/10.14569/ijacsa.2020.0110359

Maldonado, Y., & Castillo, O. (2012). Genetic design of an interval type-2 fuzzy controller for velocity regulation in a DC motor. International Journal of Advanced Robotic Systems, 9. https://doi.org/10.5772/51188

Mandal, N. K., & Sikdar, R. (2019). Comparative Study of the Speed Control of Seperately Excited DC Motor using Various Semiconductor Switches. International Journal of Emerging Technology and Advanced Engineering, 9(3).

Marks’ standard handbook for mechanical engineers. (1997). Choice Reviews Online, 34(06). https://doi.org/10.5860/choice.34-3330

Nafeh, A. A., Heikal, A., El-Sehiemy, R. A., & Salem, W. A. A. (2022). Intelligent fuzzy-based controllers for voltage stability enhancement of AC-DC micro-grid with D-STATCOM. Alexandria Engineering Journal, 61(3). https://doi.org/10.1016/j.aej.2021.07.012

Nishat, M. M., Faisal, F., Rahman, M., & Hoque, M. A. (2019). Modeling and Design of a Fuzzy Logic Based PID Controller for DC Motor Speed Control in Different Loading Condition for Enhanced Performance. 1st International Conference on Advances in Science, Engineering and Robotics Technology 2019, ICASERT 2019. https://doi.org/10.1109/ICASERT.2019.8934559

Rahmati, V., & Ghorbani, A. (2018). A Novel Low Complexity Fast Response Time Fuzzy PID Controller for Antenna Adjusting Using Two Direct Current Motors. Indian Journal of Science and Technology, 11(13), 1–8. https://doi.org/10.17485/ijst/2018/v11i13/113270

Shaker, M. M., & Al-Khashab, Y. M. B. I. (2010). Design and implementation of fuzzy logic system for DC motor speed control. EPC-IQ01 2010 - 2010 1st International Conference on Energy, Power and Control. https://doi.org/10.37917/ijeee.6.2.7

Shravan Kumar Yadav. (2015). DC Motor Position Control Using Fuzzy Proportional-Derivative Controllers With Different Defuzzification Methodsn. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE), 10(1).

Shuraiji, A. L., & Shneen, S. W. (2022). Fuzzy Logic Control and PID Controller for Brushless Permanent Magnetic Direct Current Motor: A Comparative Study. Journal of Robotics and Control (JRC), 3(6). https://doi.org/10.18196/jrc.v3i6.15974

Somwanshi, D., Bundele, M., Kumar, G., & Parashar, G. (2019). Comparison of fuzzy-PID and PID controller for speed control of DC motor using LabVIEW. Procedia Computer Science, 152. https://doi.org/10.1016/j.procs.2019.05.019

Sung, S. W., Lee, J., & Lee, I. B. (2009). Process Identification and PID Control. In Process Identification and PID Control. https://doi.org/10.1002/9780470824122

Wang, T., Wang, H., Hu, H., Lu, X., & Zhao, S. (2022). An adaptive fuzzy PID controller for speed control of brushless direct current motor. SN Applied Sciences, 4(3). https://doi.org/10.1007/s42452-022-04957-6

Xin, Z., Jinhao, L., & Dian, W. (2010). Design and simulation of fuzzy self-adjusting PID controller. 2010 International Conference on Computing, Control and Industrial Engineering, CCIE 2010, 2. https://doi.org/10.1109/CCIE.2010.125

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Published

2024-01-19

Issue

Vol. 8 No. 1 (2024): January

Section

Articles

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