DC motor control using a four-quadrant chopper based on artificial neural networks

Authors

  • Rahmad Rizki Rahmad Department of Electrical Engineering, Faculty of Engineering, Universitas Negeri Padang Author
  • Ayu Hendra Department of Electrical Engineering, Universitas Negeri Padang, Indonesia Author
  • Muldi Yuhendri Author

DOI:

https://doi.org/10.24036/q4vshv30

Keywords:

Variable speed control, Artificial Neural Network, DC motor, Four quadrant

Abstract

DC motors are widely used as drives in various industrial applications. To ensure optimal performance, precise control of DC motors is essential, including managing rotation direction, speed, braking, and starting current. This paper presents a speed control system for a DC motor using a 4-quadrant DC chopper with a neural network as the control core. The system is designed and implemented on a 12V DC motor and tested under varying speed conditions. Motor speed is adjusted in MATLAB Simulink according to operational requirements. The results confirm that the proposed DC motor speed control system, utilizing a four-quadrant chopper, functions effectively, providing accurate speed control through MATLAB Simulink.

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References

[1] A. Ma’arif and A. Çakan, “Simulation and arduino hardware implementation of dc motor control using sliding mode controller,” J. Robot. Control, vol. 2, no. 6, pp. 582–587, 2021, doi: 10.18196/jrc.26140

[2] Mykoniatis, “A real-time condition monitoring and maintenance management system for low voltage industrial motors using internet-of-things,” Procedia Manuf., vol. 42, no. 2019, pp. 450–456, 2020.

[3] Hanifah and M. Yuhendri, “Kontrol dan Monitoring Kecepatan Motor Induksi Berbasis Internet of Things,” JTEIN J. Tek. Elektro Indones., vol. 4, no. 2, pp. 519–528, 2023 [4] T. H. Mohamed, M. A. M. Alamin, and A. M. Hassan, “Adaptive position control of a cart moved by a DC motor using integral controller tuned by Jaya optimization with Balloon effect,” Comput. Electr. Eng., vol. 87, no. July, 2020, doi: 10.1016/j.compeleceng.2020.106786

[5] M. A. Mhawesh, “Performance comparison between variants PID controllers and unity feedback control system for the response of the angular position of the DC motor,” Int. J. Electr. Comput. Eng., vol. 11, no. 1, pp. 802–814, 2021, doi: 10.11591/ijece.v11i1.pp802-814.

[6] S. Hajari and O. Ray, “A Dynamic Voltage-Based Current Estimation Technique for DC Motor Speed Control Applications,” IEEE Sensors Lett., vol. 8, no. 2, 2024. [7] N. L. Manuel, N. İnanç, and M. Lüy, “Control and performance analyses of a DC motor using optimized PIDs and fuzzy logic controller,” Results Control Optim., vol. 13, no. May, 2023, doi: 10.1016/j.rico.2023.100306.

[8] F. Rahmadi and M. Yuhendri, “Kendali Kecepatan Motor DC Menggunakan Chopper DC Dua Kuadran Berbasis Kontroller PI,” JTEIN J. Tek. Elektro Indones., vol. 1, no. 2, p. 241, 2020, doi: https://doi.org/10.24036/jtein.v1i2.71.

[9] S. Sachit and B. R. Vinod, “MRAS Based Speed Control of DC Motor with Conventional PI Control —A Comparative Study,” Int. J. Control. Autom. Syst., vol. 20, pp. 1–12, 2022.

[10] I. Okoro and C. Enwerem, “Model-based Speed Control of a DC Motor Using a Combined Control Scheme,” IEEE PES/IAS PowerAfrica Conf. Power Econ. Energy Innov. Africa, PowerAfrica 2019, pp. 1–6, 2019, doi: 10.1109/PowerAfrica.2019.8928856.

[11] M. I. Esario and M. Yuhendri, “Kendali Kecepatan Motor DC Menggunakan DC Chopper Satu Kuadran Berbasis Kontroller PI,” JTEV (Jurnal Tek. Elektro dan Vokasional), vol. 6, no. 1, p. 296, 2020, doi: 10.24036/jtev.v6i1.108005.

[12] M. Yuhendri and R. Setiawan, “Implementasi DC-DC Boost Converter Menggunakan Arduino Berbasis Simulink Matlab,” JTEIN: Jurnal Teknik Elektro Indonesia, vol. 1, no. 2, pp. 144–149, 2020, doi: 10.24036/jtein.v1i2.64.

[13] D. P. M D, A. C and M. G. Umamaheswari, "Design of Neural Network Based Controller for Switching Regulator in DC-DC Boost Converters,"2023 IEEE World Conference on Applied Intelligence and Computing (AIC), Sonbhadra, India, 2023, pp. 435-440, doi: 10.1109/AIC57670.2023.10263972.

[14] G. Dewantoro and J. N. Sukamto, “Implementasi Kendali PID Menggunakan Jaringan Syaraf Tiruan Backpropagation,” Elkha, vol. 11, no. 1, p. 12, 2019, doi: 10.26418/elkha.v11i1.29959.

[15] F. A. Hizham, Y. Nurdiansyah, and D. M. Firmansyah, “Implementasi Metode Backpropagation Neural Network (BNN) dalam Sistem Klasifikasi Ketepatan Waktu Kelulusan Mahasiswa (Studi Kasus: Program Studi Sistem Informasi Universitas Jember),” Berk. Sainstek, vol. 6, no. 2, p. 97, 2018, doi: 10.19184/bst.v6i2.9254.

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Published

2025-02-27

How to Cite

DC motor control using a four-quadrant chopper based on artificial neural networks. (2025). Journal of Industrial Automation and Electrical Engineering, 1(2), 9-16. https://doi.org/10.24036/q4vshv30

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