Effect of Filtering Techniques on the Derivative Term in Fuzzy Logic Controller for DC Motor Position Control

Yıl 2025, Cilt: 15 Sayı: 3, 872 - 888, 01.09.2025

Batın Demircan , Tuğçe Yaren

https://doi.org/10.21597/jist.1592544

Öz

Direct Current (DC) motors are fundamental components in various industrial and automation systems, valued for their precision and controllability. Traditional control methods, such as Proportional-Integral-Derivative (PID) controllers, often require robust mathematical models and are susceptible to performance degradation under non-ideal conditions. This study investigates the implementation of Fuzzy Logic Controllers (FLC) for real-time DC motor position control, with a focus on analyzing the impact of different derivative approaches. To construct a comprehensive mathematical model of the DC motor system, both white-box and black-box system identification approaches were employed. The white-box method utilized physical principles of the motor, while the black-box method relied on empirical input-output data. The Transfer Function-Based Derivative (TFD) and Second-Order Filtered Derivative (SOFD) techniques are evaluated for their maintaining system responsiveness. A test setup utilizing an STM32F4 discovery kit was developed, and the performance of both derivative approaches was compared using a repeating stair sequence as the reference input. The experimental results showed that both techniques performed successfully, but the SOFD method demonstrated a more effective error reduction. The findings offer insights into derivative filtering techniques, highlighting the benefits of incorporating advanced filtering strategies in FLC-based control systems.

Anahtar Kelimeler

Dc motor , Fuzzy logic control , Derivative filter , System identification , Control Theory

Kaynakça

• Adukwu, O., Dehunsi, O. A., & Adeyeri, K. (2023). Modelling of Direct Current (DC) Motor for Performance Improvement using Model Parameter Estimation. Journal Of Engineering Research Innovation And Scientific Development, 1(2), 36–41. https://doi.org/10.61448/JERISD12235
• Akbari-Hasanjani, R., Javadi, S., & Sabbaghi-Nadooshan, R. (2014). DC motor speed control by self-tuning fuzzy PID algorithm. Http://Dx.Doi.Org/10.1177/0142331214535619, 37(2), 164–176. https://doi.org/10.1177/0142331214535619
• Al-Bargothi, S. N., Qaryouti, G. M., & Jaber, Q. M. (2019). Speed control of DC motor using conventional and adaptive PID controllers. Indonesian Journal of Electrical Engineering and Computer Science, 16(3), 1221–1228. https://doi.org/10.11591/ijeecs.v16.i3.pp1221-1228
• Asadi, F. (2018). Comparison Of Different DC Motor Modeling Techniques. Journal of Electronic Research and Application, 2(2). https://doi.org/10.26689/JERA.V2I2.333
• Frances, A., Asensi, R., & Uceda, J. (2019). Blackbox Polytopic Model with Dynamic Weighting Functions for DC-DC Converters. IEEE Access, 7, 160263–160273. https://doi.org/10.1109/ACCESS.2019.2950983
• Gebremariam, S. F., & Alemu, B. S. (2023). A Review on DC Motor Drive Controlling Schemes, Optimization Techniques and Future Trends. International Journal of Research Publication and Reviews, 4(12), 3507–3514. https://doi.org/10.55248/GENGPI.4.1223.0127
• Gu, D., Zhang, J., & Gu, J. (2015). Brushless DC motor speed control based on predictive functional control. Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015, 3456–3458. https://doi.org/10.1109/CCDC.2015.7162520
• Hidayati, Q., & Prasetyo, M. E. (2016). Pengaturan Kecepatan Motor DC dengan Menggunakan Mikrokontroler Berbasis Fuzzy-PID. JTT (Jurnal Teknologi Terpadu), 4(1). https://doi.org/10.32487/JTT.V4I1.123
• Kizir, S., Kelekci, E., & Yaren, T. (2019). Matlab Simulink Destekli Gerçek Zamanlı Kontrol Teori ve Mühendislik Uygulamaları.
• Kroičs, K., & Būmanis, A. (2024). BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content. Energies, 17(6), 1311. https://doi.org/10.3390/en17061311
• Liu, S., Zhang, H., & Pang, H. (2025). Finite-time adaptive fuzzy constrained control of uncertain switched nonlinear systems with zero dynamics. Journal of the Franklin Institute, 362(3), 107513. https://doi.org/10.1016/J.JFRANKLIN.2025.107513
• Ma’arif, A., & Çakan, A. (2021). Simulation and Arduino Hardware Implementation of DC Motor Control Using Sliding Mode Controller. Journal of Robotics and Control (JRC), 2(6), 582–587. https://doi.org/10.18196/JRC.26140
• Moaveni, B., Masoumi, Z., & Rahmani, P. (2023). Introducing Improved Iterated Extended Kalman Filter (IIEKF) to Estimate the Rotor Rotational Speed, Rotor and Stator Resistances of Induction Motors. IEEE Access, 11, 17584–17593. https://doi.org/10.1109/ACCESS.2023.3244830
• Naziris, A., Frances, A., Asensi, R., & Uceda, J. (2020). Black-box small-signal structure for single-phase and three-phase electric vehicle battery chargers. IEEE Access, 8, 170496–170506. https://doi.org/10.1109/ACCESS.2020.3024534
• Ortatepe, Z. (2023). Genetic Algorithm based PID Tuning Software Design and Implementation for a DC Motor Control System. Gazi University Journal of Science Part A: Engineering and Innovation, 10(3), 286–300. https://doi.org/10.54287/GUJSA.1342905
• Prasad, L. B., Tyagi, B., & Gupta, H. O. (2014). Optimal control of nonlinear inverted pendulum system using PID controller and LQR: Performance analysis without and with disturbance input. International Journal of Automation and Computing, 11(6), 661–670. https://doi.org/10.1007/S11633-014-0818-1/TABLES/4
• Raza, W., Adzikya, D., Mehmood, S., Wasti, S. R., Hussain, M. J., Ahmad, A., … Raza, S. (2024). Fuzzy Logic Speed Regulator for D.C. Motor Tuning. JTAM (Jurnal Teori Dan Aplikasi Matematika), 8(1), 36–49. https://doi.org/10.31764/jtam.v8i1.16919
• Sadi, S. (2020). DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller. Jurnal Teknik, 9(2). https://doi.org/10.31000/JT.V9I2.3676
• Saleem, O., & Omer, U. (2017). EKF-based self-regulation of an adaptive nonlinear PI speed controller for a DC motor. Turkish Journal of Electrical Engineering and Computer Sciences, 25(5), 4131–4141. https://doi.org/10.3906/elk-1611-311
• Sami, S. S., Obaid, Z. A., Muhssin, M. T., & Hussain, A. N. (2021). Detailed modelling and simulation of different DC motor types for research and educational purposes. International Journal of Power Electronics and Drive Systems (IJPEDS), 12(2), 703–714. https://doi.org/10.11591/IJPEDS.V12.I2.PP703-714
• Shah, J., Okasha, M., & Faris, W. (2018). Gain scheduled integral linear quadratic control for quadcopter. International Journal of Engineering & Technology, 7(4.13), 81–85. https://doi.org/10.14419/IJET.V7I4.13.21334
• Valenzuela, F. A., Ramírez, R., Martínez, F., Morfín, O. A., & Castañeda, C. E. (2020). Super-Twisting Algorithm Applied to Velocity Control of DC Motor without Mechanical Sensors Dependence. Energies 2020, Vol. 13, Page 6041, 13(22), 6041. https://doi.org/10.3390/EN13226041
• Xue, C., Zhu, H., & Yu, B. (2012). Modeling and Simulation of Parameter Self-Tuning Fuzzy PID Controller for DC Motor Speed Control System. Applied Mechanics and Materials, 195–196, 1003–1007. https://doi.org/10.4028/WWW.SCIENTIFIC.NET/AMM.195-196.1003