Optimal Tuning of PI Speed Controller in PMSM Drive: A Comparative Study of Evolutionary Algorithms

Millie Pant*, Radha Thangaraj**, Ved Pal Singh***
* Senior Lecturer ,Department of Paper Technology,Indian Institute of Technology,Roorkee,Saharanpur,U.P.
**Research Scholar,Department of paper Technology,Indian Institute of Technology,Roorkee,Saharanpur,U.P.
***Professor,Center of Excellence for Quantifiable Quality of Service,Norwegian University of Science and Technology,Norway.
****Professor,Department of Paper Technology,Indian Institute of Technology,Roorkee,Saharanpur,U.P.
Periodicity:July - September'2008
DOI : https://doi.org/10.26634/jee.2.1.328

Abstract

Optimization is one of the most discussed topics in engineering and applied research. Many engineering problems can be formulated as optimization problems. During the last few decades, many general-purpose optimization algorithms have been proposed for finding optimal solutions, some of which are; Evolution strategies, evolutionary programming, Genetic algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE).This paper presents a comparative study of three popular, Evolutionary Algorithms (EA); Genetic Algorithms, Particle Swarm Optimization and Differential Evolution for optimal tuning of Proportional Integral (PI) speed controller in Permanent Magnet Synchronous Motor (PMSM) drive. Average gain function and weighted average gain function are also considered to improve the fitness function. Numerical results show the superior performance of DE in comparison to PSO and GA.

Keywords

Evolution strategies, evolutionary programming, Genetic algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE).

How to Cite this Article?

Millie Pant, Radha Thangaraj and Ved Pal Singh (2008). Optimal Tuning of PI Speed Controller in PMSM Drive: A Comparative Study of Evolutionary Algorithms. i-manager’s Journal on Electrical Engineering, 2(1), Jul-Sep 2008, Print ISSN 0973-8835, E-ISSN 2230-7176, pp. 36-43. https://doi.org/10.26634/jee.2.1.328

References

[1] . D.M. Himmelblau, "Applied Non-Linear programming". New York: McGraw-Hill, 1972.
[2] . G.Wang, M. Zhang, X. Xu and C. Jiang, "Optimization of Controller Parameters Based on the Improved Genetic Algorithms", Proceedings of the 6th World Congress on Intelligent Control and Automation, China, pp. 21 - 23, 2006.
[3] . X. Song, Y. Zhu, C. Yin, and F. Li, "Study on the combination of genetic algorithms and ant Colony algorithms for solving fuzzy job shop scheduling problems". In Proc. of IMACS Multiconference on Computational Engineering In Systems Applications, Vol. 2, pp. 1904-1909,2006.
[4] . A. Abbasy and S. H. Hosseini, "A Novel Multi-Agent Evolutionary Programming Algorithm for Economic Dispatch Problems with Non-Smooth Cost Functions, In Proc. of IEEE Power Engineering Society General Meeting, pp. 1 -7,2007.
[5] . C, Guangyi, G. Wei and H. Kaisheng, "On Line Parameter Identification of an Induction Motor Using Improved Particle Swarm Optimization", In Proc. of Chinese Control Conference, pp. 745- 749,2007.
[6] . E. Cao and M. Lai, "An Improved Differential Evolution Algorithm for the Vehicle Routing Problem With Simultaneous Delivery and Pick-up Service", In Proc. of Third International Conference on Natural Computation, Vol. 3, pp. 436 - 440,2007.
[7] . J. Vesterstrom, R. Thomsen, "A Comparative study of Differential Evolution, Particle Swarm optimization, and Evolutionary Algorithms on Numerical Benchmark Problems," In Proc. IEEE Congr. Evolutionary Computation, Portland, OR, Jun. 20-23, (2004), pg. 1980-1987.
[8] . A. Khosla, S. Kumar and K. R. Ghosh, "A Comparison of Computational Efforts between Particle Swarm Optimization and Genetic Algorithm for Identification of Fuzzy Models", Annual Meeting of the North American Fuzzy Information Processing Society, pp. 245 - 250, 2007.
[9] . E. Elbeltagi, T. Hegazy and D. Grierson, "Comparison among five evolutionary-based optimization algorithms", Anvanced Engg. Informatics, Vol. 19, pp. 43 - 53, 2005.
[10] . Holland, J. H., "Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence", Ann Arbor, Ml: University of Michigan Press.
[11] . Goldberg, D., "Genetic Algorithms In Search Optimization and Machine Learning," Addison Wesley Publishing Company, Reading, Massachutes.
[12] . Kennedy, J. and Eberhart, R., "Particle Swarm Optimization," IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, NJ, 1995, pg. IV: 1942-1948.
[13] . Kennedy, J., "The Particle Swarm: Social Adaptation of Knowledge," IEEE International Conference on Evolutionary Computation (Indianapolis, Indiana), IEEE Service Center, Piscataway, NJ, 1997, pg.303-308.
[14] . R.C. Eberhart, Y. Shi, "Particle Swarm Optimization: developments. Applications and Resources," IEEE Int. Conference on Evolutionary Computation, 2001, pp. 81 -86.
[15] . Shi, Y. H., Eberhart, R. C., "A Modified Particle Swarm Optimizer," IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, 1998, pp. 69-73.
[16] . R. Storn and K. Price, "Differential Evolution-a simple and efficient adaptive scheme for global optimization over continuous spaces". Technical Report, International Computer Science Institute, Berkley, 1995.
[17] . D. Karaboga and S. Okdem, "A simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm", Turk J. Elec. Engin. 12(1), 2004, pp. 53- 60.
[18] . R. Storn and K. Price, "Differential Evolution a simple and efficient Heuristic for global optimization over continuous spaces". Journal Global Optimization. 11, 1997, pp. 341-359.
[19] . Rajesh Kumar, R. A Gupta and Bhim Singh, "Intelligent Tuned PID Controllers for PMSM Drive-A Critical Analysis," IEEE Int. conference, PEDES, 2006, pp. 2055-2060.
[20] . A. N. Tiwari, "Investigations on Permanent Magnet Synchronous Motor Drive", Ph.Dthesis, IITRoorkee, 2003.
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