Speed Control of DC Motor Using Particle Swarm Optimization Technique by PSO Tunned PID and FOPID

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)
© 2014 by IJETT Journal
Volume-16 Number-2
Year of Publication : 2014
Authors : Anupam Aggrawal , Akhilesh Kumar Mishra , Abdul Zeeshan


Anupam Aggrawal , Akhilesh Kumar Mishra , Abdul Zeeshan. "Speed Control of DC Motor Using Particle Swarm Optimization Technique by PSO Tunned PID and FOPID", International Journal of Engineering Trends and Technology (IJETT), V16(2),72-79 Oct 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


The objective of this work is to design a speed controller of a DC motor by finding of PID and FOPID parameters using bio-inspired optimization technique of Particle Swarm Optimization (PSO). Here, model of a DC motor is considered as a second order system for speed control. In this work bio-inspired optimization technique in controllers and their advantages over conventional methods is discussed using MATLAB/Simulink. This proposed optimization methods could be applied for higher order system also to provide better system performance with minimum errors. The main aim is to apply PSO technique to design and tune parameters of PID controller to get an output with better dynamic and static performance. The application of PSO to the PID and FOPID controller imparts it the ability of tuning itself automatically in an on-line process while the application of optimization algorithm to the PID controller makes it to give an optimum output by searching for the best set of solutions for the PID and FOPID parameters.


[1] Akhilesh K. Mishra, Anirudha Narain, “Speed Control of Dc Motor Using Particle Swarm Optimization”, International Journal of Engineering Research and Technology Vol. 1 (02), 2012, ISSN 2278 - 0181
[2] Gopal K. Dubey, “Fundamentals of Electrical Drives”, Narosa Publishing House Pvt. Ltd., 2001, chap. 6.
[3] J.G. Ziegler, N.B. Nichols, “Optimization Setting for Automatic Controller”, Trans. ASME, Vol. 64,pp. 756-769, 1942.
[4] J. Kennedy, “The Particle Swarm: Social Adaptation of Knowledge”, Proceeding of the IEEE International Conference on Evolutionary Computation, ICEC1997, Indianapolis, pp. 303-308, 1997.
[5] Ozden Ercin and Ramazan Coban, “Comparison of the Artificial Bee Colony and the Bees Algorithm for PID Controller Tuning”, Innovations in Intelligent Systems and Applications (INISTA) IEEE conference, pp. 595-598, 2011.
[6] A. Oustaloup, J. Sabatier and X. Moreau, “From fractal robustness to the CRONE approach,” ESAIM, vol. 5, pp. 177-192, 1998.
[7] D.Y. Xue and Y.Q. Chen. Advanced Mathematic Problem Solution Using MATLAB, Beijing: Tsinghua University Press, 2004.
[8] Z.F. Lv, “Time-domain simulation and design of SISO feedback control systems,” Doctoral Dissertation, National Cheng Kung University, 2004.
[9] C.N. Zhao, D.Y. Xue and Y.Q. Chen, “A fractional order PID tuning algorithm for a class of fractional order plants,” Proc of the IEEE International Conference on Mechatronics and Automation, Niagara Falls, Canada, 2005, pp. 216-221.
[10] C.A. Monje, et al. “Proposals for fractional PI?D? tuning,” The First IFAC Symposium on Fractional Differentiation and its Applications, Bordeaux, France, 2004.
[11] C.B. Ma and Y. Hori, “Design of fractional order PID controller for robust two-inertia speed control to torque saturation and load inertia variation,” IPEMC, Xi`an, China,2003.
[12] D.Y. Xue and Y.Q. Chen, “A comparative introduction of four fractional order controllers,” Proceedings of the 4th World Congress on Intelligent Control and Automation, Shanghai, China, 2002, pp. 3228-3235.
[13] Akhilesh Kumar Mishra , Vineet Kumar Tiwari , Rohit Kumar , Tushar Verma (2013). Speed Control of DC Motor Using Artificial Bee Colony Optimization Technique.Universal Journal of Electrical and Electronic Engineering, 1 , 68 - 75. doi: 10.13189/ujeee.2013.010302.

Particle Swarm Optimization; PID controller, FOPID controller; Parameter tuning.