System identification of a DC motor using PSO algorithm

Authors

  • César Duarte Industrial University of Santander
  • Jabid Quiroga Industrial University of Santander

DOI:

https://doi.org/10.17533/udea.redin.14720

Keywords:

system identification, particle swarm optimization, intelligent optimization

Abstract

In this paper an application of Particle Swarm Optimization (PSO) algorithm is presented as strategy for parameters determination of a grey box system. This system identification process is implemented based on the open loop step response for a DC motor. The vector space is bounded via step time response to speed up the parameter identification. The PSO algorithm is implemented and validated using the PSOt Matlab® toolbox developed by Brian Birge.

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Author Biographies

César Duarte, Industrial University of Santander

School of Electrical, Electronic and Telecommunications Engineering.

Jabid Quiroga, Industrial University of Santander

School of Mechanical Engineering.

References

G. Panda, D. Mohanty, B. Majhi, G. Sahoo. Identification of nonlinear systems using particle swarm optimization technique. Evolutionary Computation. CEC. IEEE Congress Singapore. Sept. 2007. pp.3253-3257.

X. Peng, G.K. Venayagamoorthy, K. A. Corzine. Combined Training of Recurrent Neural Networks with Particle Swarm Optimization and Backpropagation Algorithms for Impedance Identification. Swarm Intelligence Symposium. SIS. IEEE. Honolulu 1-5 April 2007. pp.9-15.

Z. Dong, P. Han, D. Wang, S. Jiao. Thermal Process System Identification Using Particle Swarm Optimization. IEEE International Symposium on Industrial Electronics. Montreal. Vol.1. 2006. pp.194-198.

L. Liu. Robust fault detection and diagnosis for permanent magnet synchronous motors. Ph.D. dissertation. Dept. Mech. Eng. Florida State University. Tallahassee (FL) 2006. pp. 83-105.

J. Kennedy, R. Eberhart. “Particle Swarm Optimization,” Proceedings of IEEE International Conference on Neural Networks. Perth (Australia) Vol. 4. 1995. pp. 1942-1948.

Y. Shi. Particle Swarm Optimization. Ed. Electronic Data Systems Inc. IEEE Neural Networks Society. Kokomo (USA). 2004. pp. 8-13.

J. G. Ziegler, N. B. Nichols. “Optimum settings for automatic controllers”. Trans. A.S.M.E. Vol. 64. 1942. pp. 759-765.

Birge, B., “PSOt - a particle swarm optimization toolbox for use with Matlab,” Swarm Intelligence Symposium. 2003. SIS ‘03. Proceedings of the 2003 IEEE. Indianapolis. April 24-26. 2003. pp. 182-186.

http://www.mathworks.com/matlabcentral/fileexchange/7506. Consultada el 16 de diciembre de 2008.

Published

2013-03-01

How to Cite

Duarte, C., & Quiroga, J. (2013). System identification of a DC motor using PSO algorithm. Revista Facultad De Ingeniería Universidad De Antioquia, (55), 116–124. https://doi.org/10.17533/udea.redin.14720

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