Abstract
DC motors are an important component and are used in industrial machines and engineering applications. The position control of DC motor is useful in precision control systems. This study aims to present a comparative analysis of different controllers and to tune these controllers using metaheuristic algorithms for DC motor position control. The position control is modeled using its transfer function. In this study, the performance of PID, fractional-PID (F-PID) and PID with filter coefficient Ni (PID-N) has been compared. The controller’s parameters, i.e., Kp, Ki, Kd, Ni, μ, λ have been tuned by using global neighborhood algorithm (GNA) and bat algorithm (BA). The optimization performance of the chosen algorithms is compared using transient response analysis. The results obtained after performing the simulation show that the overall performance of PID-N controller is better compared to the other controllers, and GNA does better overall tuning of controller parameters compared to BA. All simulations were done using MATLAB/Simulink.
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References
Neenu Thomas, D. P. P. (2009). Position control of DC motor using genetic algorithm based PID controller. Proc World Congr Eng 2009
Aamir, M. (2013). On replacing PID controller with ANN controller for DC motor position control. Int J Res Stud Comput, 2, 21–29. https://doi.org/10.5861/ijrsc.2013.236
Moran, M. E. F., & Viera, N. A. P. (2018). Comparative study for DC motor position controllers. In 2017 IEEE 2nd Ecuador Tech Chapters Meet ETCM 2017 2017-Janua:1–6. https://doi.org/10.1109/ETCM.2017.8247475
Flores-Morán, E., Yánez-Pazmiño, W., & Barzola-Monteses, J. (2018). Genetic algorithm and fuzzy self-tuning PID for DC motor position controllers. Proc 2018 19th Int Carpathian Control Conf ICCC 2018, 162–168. https://doi.org/10.1109/CarpathianCC.2018.8399621
Duman, S., Maden, D., & Güvenç, U. (2011). Determination of the PID controller parameters for speed and position control of DC motor using gravitational search algorithm. In ELECO 2011—7th Int Conf Electr Electron Eng.
Dhieb, Y., Yaich, M., Guermazi, A., & Ghariani, M. (2019). PID controller tuning using ant colony optimization for induction motor. J Electr Syst, 15, 133–141.
Manoj Kushwah PAP (2014) Tuning of PID controller for speed control of DC motor using soft computing techniques—A review. Adv Electron Electr Eng, 4.
Allaoua, B., & Mebarki, B. (2012). Intelligent PID DC motor speed control alteration parameters using particle swarm optimization. Artif Intell Resour Control Autom Eng, 3–14. https://doi.org/10.2174/978160805126711201010003
Meshram Rohit, P. M., & Kanojiya G. (2012). Method for speed control of DC Motor. Int Conf Adv Eng Sci Manag, 117–122.
Das, K. R., Das, D., & Das, J. (2016). Optimal tuning of PID controller using GWO algorithm for speed control in DC motor. Int Conf Soft Comput Tech Implementations, ICSCTI, 2015, 108–112. https://doi.org/10.1109/ICSCTI.2015.7489575
Jain, R. V. , & MVA, ASJ. (2016). Tuning of fractional order PID controller using particle swarm optimization technique for DC motor speed control, 006, 6–9
Hekimoglu, B. (2019). Optimal Tuning of Fractional Order PID Controller for DC Motor Speed Control via Chaotic Atom Search Optimization Algorithm. IEEE Access, 7, 38100–38114. https://doi.org/10.1109/ACCESS.2019.2905961
Premkumar, K., & Manikandan, B. V. (2016). Bat algorithm optimized fuzzy PD based speed controller for brushless direct current motor. Eng Sci Technol an Int J, 19, 818–840. https://doi.org/10.1016/j.jestch.2015.11.004
Gozde, H., Taplamacioglu, M. C., & Ari, M. (2017). Simulation study for global neighborhood algorithm based optimal automatic voltage regulator (AVR) system. In ICSG 2017—5th Int Istanbul Smart Grids Cities Congr Fair (pp. 46–50). https://doi.org/10.1109/SGCF.2017.7947634
Alazzam A. W. H. (2013). A new optimization algorithm for combinatorial problems. Int J Adv Res Artif Intell, 2, 63–68. https://doi.org/10.14569/ijarai.2013.020510
Yang, X. S. (2010). A new metaheuristic Bat-inspired Algorithm. Stud Comput Intell, 284, 65–74. https://doi.org/10.1007/978-3-642-12538-6_6
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Singhal, A., Mittal, D., Roy, R., Dahiya, P. (2022). Comparative Analysis for Optimal Tuning of DC Motor Position Control System. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_60
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DOI: https://doi.org/10.1007/978-981-16-3346-1_60
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