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Speed Control of DC Motor with MRPID Controller in the Presence of Noise

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Abstract

Speed control of a DC motor has always been a challenge because of its variable torque. But it becomes more challenging when noise enters the system at its input. Therefore, there is a need of more advanced controllers. In this paper, a multi-resolution proportional integral derivative (MRPID) controller has been proposed to be utilized to control the speed of a DC motor. It works well even in the presence of noise as compared to the conventional PID controller. Also, performance of a PID controller deteriorates when nonlinearity or uncertainty arises in the system. This degraded performance can be improved by utilizing the multi-resolution property of wavelets, which decomposes the error signal into various frequency components. Further, wavelet coefficients of these decompositions are used to generate the control signal for controlling speed of a DC motor. In this paper, performances of a MRPID, a fractional order PID (FOPID) and a conventional PID controllers are compared in the presence of noise for speed control of a DC motor. The results obtained using a MRPID controller are observed to be better in terms of improved transient characteristics and disturbance rejection for a DC motor as compared to those obtained with PID and FOPID controllers.

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References

  1. Kamarudin, M. N., & Rozali, S. M. (2008). Simulink implementation of digital cascade control DC motor model—A didactic approach. In 2nd IEEE international conference on power and energy, Johor Baharu, Malaysia.

  2. Hardik, S., Jain, A., Palak, S., Agrawal, K., & Tibrewal, M. K.  H. (2019). DC motor speed control using PID controller, IR sensor and PWM hysteresis. In International journal of innovative technology and exploring engineering (IJITEE) (Vol. 8). ISSN: 2278-3075.

  3. Allaoua, B., Abderrahmani, A., Gasbaoui, B., & Nasri, A. (2008). The efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control. Serbian Journal of Electrical Engineering, 5(2), 247–262.

    Article  Google Scholar 

  4. Ahmed, A. M., Ali-Eldin, A., Elksasy, M. S., & Areed, F. F. (2015). Brushless DC motor speed control using both PI controller and fuzzy PI controller. International Journal of Computer Applications, 109(10), 29–35.

  5. Hameed, W. I., & Mohamad, K. A. (2012). Speed control of separately excited DC motor using fuzzy neural model reference controller. International Journal of Instrumentation and Control Systems (IJICS), 2(4), 27–39.

  6. Khuntia, S. R., Mohanty, K. B., Panda, S., & Ardil, C. (2010). A comparative study of P-I, I-P, fuzzy and neuro-fuzzy controllers for speed control of DC motor drive. International Journal of Electrical and Computer Engineering, 5, 287–291.

  7. Kannan, P., Natarajan, S. K., & Dash, S. S. (2013). Design of fuzzy logic controller for online speed regulation of DC motor using Pwm technique based on laboratory virtual instrument engineering workbench. Journal of Computer Science, 9(8), 990–997 Science Publications.

    Article  Google Scholar 

  8. Kushwah, R., & Wadhwani, S. (2013). Speed control of separately excited DC motor using fuzzy logic controller. International Journal of Engineering Trends and Technology (IJETT), 4(6), 2518–2523.

  9. Tipsuwan, Y., Aiemchareon, S., & Neuro-Fuzzy, A. (2005). Network-based controller for DC motor speed control. In 31st annual conference of industrial electronics society, IEEE IECON’05, Raleigh.

  10. Agrawal, S., Kumar, V., Rana, K. P. S., & Mishra, P. (2015). Optimization of PID controller with first order noise filter. In International conference on futuristic trends on computational analysis and knowledge management (ABLAZE), Greater Noida.

  11. Chen, K. (2013). PID control based on Kalman Filter for DC motor. Advanced Materials Research, 850–851, 612–615. https://doi.org/10.4028/www.scientific.net/AMR.850-851.612.

    Article  Google Scholar 

  12. Kanungo, A., Mittal, M., & Dewan, L. (2020). Critical analysis of optimization techniques for a MRPID thermal system controller. IETE Journal of Research. https://doi.org/10.1080/03772063.2020.1808092

  13. Yilmaz, M., Tuncay, R. N., Ustun, O., & Krein, T. P. (2009). Sensorless control of brushless DC motor based on wavelet theory. Electric Power Components and Systems, 37(10), 1063–1080. https://doi.org/10.1080/15325000902953377.

    Article  Google Scholar 

  14. Kanungo, A., Mittal, M., & Dewan, L. (2020). Wavelet based PID controller using GA optimization and scheduling for feedback systems. Journal of Interdisciplinary Mathematics, 23(1), 145–152. https://doi.org/10.1080/09720502.2020.1721708

  15. Jain, H. S., Palak, A., Agrawal, S., Tibrewal, K., & Hota, M. K. (2019). DC motor speed control using PID controller, IR sensor and PWM hysteresis. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6), 781–786.

  16. Kumar, V., & Mittal, M. (2019). Optimal speed control of DC servomotor in the presence of disturbance and noise using stochastic algorithm. In Proc. IEEE international conference on trends in electronics and informatics (Vol. 23, pp. 1–6). ICOEI.

  17. Vajpayee, V., Mukhopadhyay, S., & Tiwari, A. P. (2016). A multi resolution wavelet based subspace identification. An International Journal of IFAC-PapersOn Line, 49(1), 247–253.

    Google Scholar 

  18. Antoine, J. P. (2002). Wavelet transforms and their applications wavelet transforms and their applications. Physics Today, 56(4), 68–68.  Lokenath Debnath, Birkhäuser, Boston. ISBN 0-8176-4204-8. https://doi.org/10.1063/1.1580056

  19. Tolentino, J. A. C., Velasco, L. E. R., & Rivera, M. A. E. (2010). A selft-tuning of a wavelet PID controller. In Proc. 20th international conf. on electr., comm. and computer, Mexico (pp. 73–78) [online]. https://doi.org/10.1109/CONIELECOMP.2010.5440793

  20. Khan, M. A. S. K., & Rahman, M. A. (2010). Implementation of a wavelet-based MRPID controller for benchmark thermal system. IEEE Transactions on Industrial Electronics, 57(12), 4160–4169.

  21. Gomez, O. I., R.Velasco, L. E., & Lamont, J. G. (2011). Implementation of different wavelets in an auto-tuning wavenet PID controller and its application to a DC motor. In Proc. 2011 IEEE electronics, robotics and automotive mechanics conference, Morelos, Mexico [online]. https://doi.org/10.1109/CERMA.2011.55

  22. Khan, M. A. S. K., & Rahman, M. A. (2011). Implementation of wavelet-based controller for battery storage system of hybrid electric vehicles. IEEE Transactions on Industry Applications, 47(5), 2241–2249.

    Article  Google Scholar 

  23. Parvez, S., & Gao, Z. (2005). A wavelet-based multiresolution PID controller. IEEE Transactions on Industry Applications, 41(2), 537–543.

    Article  Google Scholar 

  24. Hussain, I., Ranjan, S., Das, D. C., & Sinha, N. (2017). Performance analysis of flower pollination algorithm optimized PID controller for Wind-PV-SMES-BESS-diesel autonomous hybrid power system. International Journal of Renewable Energy Research (IJRER), 7(2), 643–651.

    Google Scholar 

  25. Khodabakhshian, A., & Hooshmand, R. (2009). A new PID controller design for automatic generation control of hydro power systems. International Journal of Electrical Power & Energy Systems, 32(5), 375–382.

    Article  Google Scholar 

  26. Parvez, S. (2003). Advanced control techniques for motion control problem (Ph.D. dissertation). Cleveland State University.

  27. Tipsuwan, Y., & Aiemchareon, S. (2005). A neuro-fuzzy network-based controller for DC motor speed control. In Proc. 31st annual conference of industrial electronics society, IEEE IECON’05, Raleigh [online]. 10.1109/IECON.2005.1569287

  28. Emhemed, A. A. A., & Mamat, R. B. (2012). Modelling and simulation for industrial DC motor using intelligent control. Procedia Engineering, 41, 420–425.

    Article  Google Scholar 

  29. Gheibi, A., Ali, S. M., & Farsangi, M. M. (2014). Comparing performance of PID and fuzzy controllers in the present of noise for a Photovoltaic System. Journal of Mathamatics and Computer Science (JMCS) [online]. https://doi.org/10.22436/jmcs.09.01.08

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Correspondence to Abhas Kanungo.

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Kanungo, A., Mittal, M., Dewan, L. et al. Speed Control of DC Motor with MRPID Controller in the Presence of Noise. Wireless Pers Commun 124, 893–907 (2022). https://doi.org/10.1007/s11277-021-09388-x

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