Identification of Transfer Function Parameters of Brushless DC Motor Using Particle Swarm Algorithm
Subject Areas : electrical and computer engineering
Ahmad Shirzadi
1
,
Arash Dehestani Kolagar
2
,
Mohammad Reza Alizadeh Pahlavani
3
1 - Malek Ashtar University of Technology
2 - Malek Ashtar University of Technology
3 - malek ashtar
Keywords: Particle swarm optimization algorithm, brushless DC motor, transfer function, parameter estimation,
Abstract :
So far, comprehensive and extensive studies have been conducted on the brushless DC motor (BLDC), and a part of these studies focuses on the estimation of the parameters of the transfer function of this motor. Estimation of BLDC motor transfer function parameters is essential to study motor performance and predict its behavior. Therefore, an efficient, accurate and reliable parameter estimation method is needed. In this article, the problem of estimating the parameters of the transfer function of the inverter-fed BLDC motor set has been solved using particle swarm algorithms (PSO). The results of using this algorithm have been compared with the results of other optimization algorithms. The comparison of these results has shown that the PSO algorithm is an efficient, accurate and reliable method for solving the transfer function parameter estimation problem.
[1] C. Xia, Permanent Magnet Brushless DC Motor Drives and Controls, Wiley, 2012.
[2] J. Cortés-Romero, A. Luviano-Juarez, R. Alvarez-Salas, and H. Sira-Ramírez, "Fast identification and control of an uncertain brushless DC motor using algebraic methods," in Proc. 12th IEEE Int. Power Electronics Congress, pp. 9-14, San Luis Potosi, Mexico, 22-25 Aug. 2010.
[3] C. L. Xia, Permanent Magnet Brushless DC Motor Drives and Controls, John Wiley & Sons, 2012.
[4] T. Li and J. Zhou, "High-stability position-sensorless control method for brushless DC motors at low speed," IEEE Trans. on Power Electronics, vol. 34, no. 5, pp. 4895-4903, May 2019.
[5] J. U. Liceaga-Castro, I. I. Siller-Alcalá, J. Jaimes-Ponce, R. A. Alcántara-Ramírez, and E. Arévalo Zamudio, "Identification and real time speed control of a series DC motor," Mathematical Problems in Engineering, vol. 2017, Article ID: 7348263, 2017.
[6] A. K. Wallace and R. Spee, "The effects of motor parameters on the performance of brushless DC drives," IEEE Trans. on Power Electronics, vol. 5, no. 1, pp. 2-8, Jan. 1990.
[7] Y. A. Apatya, A. Subiantoro, and F. Yusivar, "Design and prototyping of 3-phase BLDC motor," in Proc. 15th IEEE Int. Conf. on Quality in Research (QiR): Int. Symp. on Electrical and Computer Engineering, pp. 209-214, Nusa Dua, Bali, Indonesia 24-27 Jul. 2017.
[8] B. Vaseghi, N. Takorabet, and F. Meibody-Tabar, "Fault analysis and parameter identification of permanent-magnet motors by the finite-element method," IEEE Trans. on Magnetics, vol. 45, no. 9, pp. 3290-3295, Sept. 2009.
[9] IEEE Std. 1812-2014, IEEE Trial-Use Guide for Testing Permanent Magnet Machines, pp. 1-56, 2015.
[10] R. Beloiu, "Dynamic determination of DC motor parameters-simulation and testing," in Proc. of the 6th Int. Conf. on Electronics, Computers and Artificial Intelligence, ECAI'14, pp. 13-18, Bucharest, Romania, 23-25 Oct. 2014.
[11] R. Shanmugasundram, K. M. Zakaraiah, and N. Yadaiah, "Effect of parameter variations on the performance of direct current (DC) servomotor drives," J. of Vibration and Control, vol. 19, no. 10, pp. 1575-1586, 2013.
[12] I. Virgala and M. Kelemen, "Experimental friction identification of a DC motor," International J. of Mechanics and Applications, vol. 3, no. 1, pp. 26-30, 2013.
[13] S. A. Odhano, et al., "Identification of three-phase IPM machine parameters using torque tests," IEEE Trans. on Industry Applications, vol. 53, no. 3, pp. 1883-1891, May/June. 2017.
[14] C. Xiang, X. Wang, Y. Ma, and B. Xu, "Practical modeling and comprehensive system identification of a BLDC motor," Mathematical Problems in Engineering, vol. 2015, Article ID: 879581, 2015.
[15] S. Cong, G. Li, and X. Feng, "Parameters identification of nonlinear DC motor model using compound evolution algorithms," in Proc. of the World Congress on Engineering, vol. 1, 6 pp. 15-20, London, UK, 30 Jun.-2 Jul. 2010.
[16] I. Anshory, I. Robandi, and M. Ohki, "System identification of BLDC motor and optimization speed control using artificial intelligent," International J. of Civil Engineering and Technology, vol. 10, no. 7, pp. 1-13, 2019.
[17] K. Balamuruga and R. Mahalakshmi, "Parameter identification in BLDC motor using optimization technique," J. of Applied Science and Engineering Methodologies, vol. 3, no. 2, pp. 465-470, 2017.
[18] I. D. Landau and G. Zito, Digital Control Systems: Design, Identification and Implementation, Springer, 2006.
[19] E. B. Siqueira, J. L. Mor, R. Z. Azzolin, and V. M. de Oliveira, "Algorithm to identification of parameters and automatic re-project of speed controller of BLDC motor," IFAC-PapersOnLine, vol. 48, no. 19, pp. 256-261, 2015.
[20] Y. Yang, H. Chen, A. A. Heidari, and A. H. Gandomi, "Hunger games search: visions, conception, implementation, deep analysis, perspectives, and towards performance shifts," Expert Systems with Applications, vol. 177, Article ID: 114864, Sept. 2021.
[21] A. A. Heidari, et al., "Harris hawks optimization: algorithm and applications," Future Generation Computer Systems, vol. 97, pp. 849-872, Aug. 2019.
[22] I. Ahmadianfar, A. A. Heidari, A. H. Gandomi, X. Chu, and H. Chen, "RUN beyond the metaphor: an efficient optimization algorithm based on Runge Kutta method," Expert Systems with Applications, vol. 181, Article ID: 115079, Nov. 2021.
[23] S. Li, H. Chen, M. Wang, A. A. Heidari, and S. Mirjalili, "Slime mould algorithm: a new method for stochastic optimization," Future Generation Computer Systems, vol. 111, pp. 300-323, Oct. 2020.
[24] A. H. Wright, "Genetic algorithms for real parameter optimization," Foundations of Genetic Algorithms, vol. 1, pp. 205-218, 1991.
[25] J. Ronkkonen, S. Kukkonen, and K. V. Price, "Real-parameter optimization with differential evolution," in Proc. IEEE Congress on Evolutionary Computation, vol. 1, pp. 506-513, Edinburgh, UK, 2- 5 Sept. 2005.
[26] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proc. IEEE Int. Conf. on Neural Networks, ICNN'95, vol. 4, pp. 1942-1948, 1995.
[27] D. Kumpanya, S. Thaiparnat, and D. Puangdownreong, "Parameter identification of BLDC motor model via metaheuristic optimization techniques," Procedia Manufacturing, vol. 4, pp. 322-327, 2015.
[28] P. Erdogmus, Particle Swarm Optimization with Applications, IntechOpen, 2018.
[29] M. Clerc and J. Kennedy, "The particle swarm-explosion, stability, and convergence in a multidimensional complex space," IEEE Trans. on Evolutionary Computation, vol. 6, no. 1, pp. 58-73, Feb. 2002.
